Tesis sobre el tema "Stochastic Fokker-Planck"

A stochastic process or sometimes called random process is the counterpart to a deterministic process in theory. A stochastic process is a random field, whose domain is a region of space, in other words, a random function whose arguments are drawn from a range of continuously changing values. In this case, Instead of dealing only with one possible 'reality' of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths are more probable and others less. However, in discrete time, a stochastic process amounts to a sequence of random variables known as a time series. Over the past decades, the problems of synergetic are concerned with the study of macroscopic quantitative changes of systems belonging to various disciplines such as natural science, physical science and electrical engineering. When such transition from one state to another take place, fluctuations i.e. (random process) may play an important role. Fluctuations in its sense are very common in a large number of fields and nearly every system is subjected to complicated external or internal influences that are often termed noise or fluctuations. Fokker-Planck equation has turned out to provide a powerful tool with which the effects of fluctuation or noise close to transition points can be adequately be treated. For this reason, in this thesis work analytical and numerical methods of solving Fokker-Planck equation, its derivation and some of its applications will be carefully treated. Emphasis will be on both for one variable and N- dimensional cases.

This thesis makes two contributions to the solar literature. The first is the development and application of a formal statistical framework for describing short-term (daily) variation in the level of magnetic activity on the Sun. Modelling changes on this time-scale is important because rapid developments of magnetic structures on the sun have important consequences for the space weather experienced on Earth (Committee On The Societal & Economic Impacts Of Severe Space Weather Events, 2008). The second concerns how energetic particles released from the Sun travel through the solar wind. The contribution from this thesis is to resolve a mathematical discrepancy in theoretical models for the transport of charged particles.

Fokker-Planck equations, along with stochastic differential equations, play vital roles in physics, population modeling, game theory and optimization (finite or infinite dimensional). In this thesis, we study three topics, both theoretically and computationally, centered around them. In part one, we consider the optimal transport for finite discrete states, which are on a finite but arbitrary graph. By defining a discrete 2-Wasserstein metric, we derive Fokker-Planck equations on finite graphs as gradient flows of free energies. By using dynamical viewpoint, we obtain an exponential convergence result to equilibrium. This derivation provides tools for many applications, including numerics for nonlinear partial differential equations and evolutionary game theory. In part two, we introduce a new stochastic differential equation based framework for optimal control with constraints. The framework can efficiently solve several real world problems in differential games and Robotics, including the path-planning problem. In part three, we introduce a new noise model for stochastic oscillators. With this model, we prove global boundedness of trajectories. In addition, we derive a pair of associated Fokker-Planck equations.

La discesa stocastica del gradiente (SGD) è alla base degli algoritmi di ottimizzazione di reti di Deep Learning più usati in AI, dal riconoscimento delle immagini all’elaborazione del linguaggio naturale. Questa tesi si propone di descrivere un modello basato sull’equazione di Fokker-Planck della dinamica del SGD. Si introduce la teoria dei processi stocastici, con particolare enfasi sulle equazioni di Langevin e sull’equazione di Fokker-Planck. Si mostra come il SGD minimizzi un funzionale sulla densità di probabilità dei pesi, non dipendente direttamente dalla funzione di costo. Infine si discutono le implicazioni di questa inferenza variazionale ottenuta dal SGD.

The system of three differential equations describing the stochastic condensation process under quasiequilibrium equilibrium conditions is constructed taking into account the additive and multiplicative components. The phase diagram of the system states was constructed and analyzed. The domains of the existence of the condensation processes, disassembly of previously deposited material, and the complete evaporation were determined. The distribution density of the concentration of adsorbed atoms was defined. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/34910

We derive the generalized Fokker-Planck equation for the probability density function of the nanoparticle magnetic moment driven by Poisson white noise. Our approach is based on the reduced stochastic Landau-Lifshitz equation in which this noise is included into the effective magnetic field. We take into account that the magnetic moment under the noise action can change its direction instantaneously and show that the generalized equation has an integro-differential form. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/35373

The primary objective of this thesis is to make a quantitative study of complex biological networks. Our fundamental motivation is to obtain the statistical dependency between modules by injecting external noise. To accomplish this, a deep study of stochastic dynamical systems would be essential. The first chapter is about the stochastic dynamical system theory. The classical estimation of invariant measures of Fokker-Planck equations is improved by the level set method. Further, we develop a discrete Fokker-Planck-type equation to study the discrete stochastic dynamical systems. In the second part, we quantify systematic measures including degeneracy, complexity and robustness. We also provide a series of results on their properties and the connection between them. Then we apply our theory to the JAK-STAT signaling pathway network.

In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron], Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden. Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert. IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pulsverhalten oft nicht beschreiben und müssen dazu erweitert werden. Im erste Teil dieser Arbeit wird eine Theorie zur Berechnung der Pulszugleistungsspektren von stochastischen, multidimensionalen IF Neuronen entwickelt. Ausgehend von der zugehörigen Fokker-Planck-Gleichung werden partiellen Differentialgleichung abgeleitet, deren Lösung sowohl die stationäre Wahrscheinlichkeitsverteilung und Feuerrate, als auch das Pulszugleistungsspektrum beschreibt. Im zweiten Teil wird eine Theorie für große, spärlich verbundene und homogene Netzwerke aus IF Neuronen entwickelt, in der berücksichtigt wird, dass die zeitlichen Korrelationen von Pulszügen selbstkonsistent sind. Neuronale Eingangströme werden durch farbiges Gaußsches Rauschen modelliert, das von einem mehrdimensionalen Ornstein-Uhlenbeck Prozess (OUP) erzeugt wird. Die Koeffizienten des OUP sind vorerst unbekannt und sind als Lösung der Theorie definiert. Um heterogene Netzwerke zu untersuchen, wird eine iterative Methode erweitert. Im dritten Teil wird die Fokker-Planck-Gleichung auf Binärentscheidungen von Diffusionsentscheidungsmodellen [Engl. diffusion-decision models (DDM)] angewendet. Explizite Gleichungen für die Entscheidungszugstatistiken werden für den einfachsten und analytisch lösbaren Fall von der Fokker-Planck-Gleichung hergeleitet. Für nichtliniear Modelle wird die Schwellwertintegrationsmethode erweitert.
This thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.

Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolution of the the probability density function of the often high-dimensional state space. The CME is approximated by a partial differential equation (PDE), the Fokker-Planck equation and solved numerically. Direct solution of the CME rapidly becomes computationally expensive for increasingly complex biological models, since the state space grows exponentially with the number of dimensions. Adaptive numerical methods can be applied in time and space in the PDE framework, and error estimates of the approximate solutions are derived. A method for splitting the CME operator in order to apply the PDE approximation in a subspace of the state space is also developed. The performance is compared to the most widely spread alternative computational method.

L'objectif de notre thèse de

doctorat est d’étudier et de décrire les propriétés chimiques et mé-

caniques du moteur moléculaire F1 -ATPase. Le moteur F1 -ATPase

est un moteur rotatif, d’aspect sphérique et d’environ 10 nanomètre

de rayon, qui utilise l’énergie de l’hydrolyse de l’ATP comme car-

burant moléculaire.

Des questions fondamentales se posent sur le fonctionnement de

ce moteurs et sur la quantité de travail qu’il peut fournir. Il s’agit

de questions qui concernent principalement la thermodynamique

des processus irréversibles. De plus, comme ce moteur est de

taille nanométrique, il est fortement influencé par les fluctuations

moléculaires, ce qui nécessite une approche stochastique.

C’est en créant deux modéles stochastiques complémentaires de

ce moteur que nous avons contribué à répondre à ces questions

fondamentales.

Le premier modèle discuté au chapitre 5 de la thèse, est un mod-

èle continu dans le temps et l’espace, décrit par des équations de

Fokker-Planck, est construit sur des résultats expérimentaux.

Ce modèle tient compte d’une description explicite des fluctua-

tions affectant le degré de liberté mécanique et décrit les tran-

sitions entre les différents états chimiques discrets du moteur,

par un processus de sauts aléatoires entre premiers voisins. Nous

avons obtenus des résultats précis concernant la chimie d’hydrolyse

et de synthèse de l’ATP, et pour les dépendences du moteur en les

différentes variables mécaniques, à savoir, la friction et le couple

de force extérieur, ainsi que la dépendence en la température.

Les résultats que nous avons obtenus avec ce modèle sont en ex-

cellent accord avec les observations expérimentales.

Le second modèle est discret dans l’espace et continu dans le

temps et est décrit dans le chapitre 6. L’analyse des résultats

obtenus par simulations numériques montre que le modèle est

en accord avec les observations expérimentales et il permet en

outre de dériver des grandeurs thermodynamiques analytique-

ment, décrites au chapitre 4, ce que le modèle continu ne permet

pas.

La comparaison des deux modèles révele la nature du fonction-

nement du moteur, ainsi que son régime de fonctionnement loin

de l’équilibre. Le second modèle a éte soumis récemment pour

publication.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

In questo lavoro studiamo sistemi Hamiltoniani sottoposti a perturbazioni stocastiche e ne analiziamo applicazioni a problemi relativi all’apertura dinamica di LHC. Il moto betatronico non lineare di particelle sottoposte all’azione degli elementi magnetici di un acceleratore é ben descritto da sistemi Hamiltoniani perturbati. Una approfondita comprensione della dinamica di singola particella é cruciale per il buon funzionamento della macchina. In particolare il problema della perdita di intensità del fascio é stato affrontato in passato e sono state proposte leggi di scala per l’apertura dinamica basate su stime di tipo Nekhoroshev. Noi studiamo lo scenario in cui una Hamiltoniana integrabile é sottoposta a piccole perturbazioni stocastiche, rappresentanti l’interazione con l’ambiente, la cui ampiezza dipende solo dallo stato dinamico della particella. Successivamente impostiamo un’equazione di Fokker-Planck per la densitá di probabilitá della variabile di azione per studiare la diffusione del sistema debolmente caotico introdotto sopra e proponiamo come coefficienti di diffusione stime di tipo Nekhoroshev e leggi di potenza, calibrate in certa misura fenomenologicamente. Segue una nostra derivazione di quantitá semi-analitiche interessanti nello studio della diffusione di particelle a partire dall’equazione di Fokker-Planck. In fine relazioniamo il modello diffusivo cosí costruito con dati sperimentali, comparando le nostre stime con misure su perdite di intensitá nei fasci eseguite a LHC.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Desenvolvemos uma análise quântica de uma cavidade pendular, utilizando a representação P positiva, mostrando que o estado quântico do movimento de um espelho,um objeto macroscópico, tem efeitos notáveis na dinâmica deste sistema. Este foi proposto anteriormente como um candidato para medidas quanticamente limitadas de pequenos deslocamentos do espelho devido à pressão de radiação, para a produção de estados com emaranhamento entre espelho e o campo e também para estados de superposição do espelho. Contudo, quando tratamos o espelho oscilante como um oscilador quântico encontramos que este sistema sempre oscila, não possui estados estacionários e exibe incertezas na posição e no momento que são tipicamente maiores que os valores médios. Isto significa que a análise linearizada das flutuações realizadas predominantemente para prever estes estados quânticos são de uso limitado. Achamos que a acuracidade alcançável na realização das medidas é muito pior do que o limite quântico padrão, devido ao ruído térmico, que para parâmteros experimentais típicos é enorme mesmo em 2mK.
We perform a quantum mechanical analysis of a pendular cavity, using the positive-P representation, showing that the quantum state of the moving mirror, a microscopic object, has noticeable eects on the dynamics. This system was previously been proposed as a candidate for the quantum-limited measurement of small displacements os the mirror due to radiation pressure, for the production of states with entanglement between the mirror and the field, and even for superposition states of the mirror. However, when we treat the oscillating mirror quantum mechanically, we find that it always oscillates, has no stationary steady-state, and exhibits uncertainties in position and momentum wich are typically large than the mean values. This means that previous linearised fluctuation analyses wich have been used to predict these highly quantum states are of limited use. We find that achievable accuracy in measurement is far worse than the standard quantum limit due to thermal noise, which, for typical experimental parameters, is overwhelming even at 2mK.

Lagrangian stochastic models are a largely used tool in the study of passive substances dispersion inside the Atmospheric Boundary Layer. Its application is related to the trajectory computation of thousands of particles, that numerically simulate the dispersion of suspense substances in the atmosphere. In this study, the basic concepts related to the Lagrangian stochastic modelling are presented and discussed together with its main characteristics and its computational implementation, to the study of particles dispersion in the atmosphere. In a computational experiment, the obtained results are compared with observational data from the TRACT experiment, that took place in Europe in 1992. The input data needed for the dispersion model are extracted from simulations with the numerical weather forecast model RAMS. Dispersion over Rio de Janeiro region is also tested in a second experiment.
Modelos Lagrangianos estocásticos constituem ferramenta muito utilizada no estudo da dispersão de substâncias passivas na Camada Limite Atmosférica. Sua aplicação consiste em calcular a trajetória de milhares de partículas, que simulam numericamente a dispersão de uma substância em suspensão na atmosfera. Nesta tese, são apresentados e discutidos os conceitos básicos relacionados à Modelagem Lagrangiana Estocástica de Partículas, bem como suas principais características e sua implementação computacional, para o estudo da dispersão de partículas na atmosfera. Numa experimentação computacional, comparam-se os resultados obtidos com dados observacionais provenientes do experimento TRACT, realizado na Europa em 1992. Os dados de entrada necessários ao modelo de dispersão são extraídos de simulações do modelo de previsão numérica do tempo RAMS. A dispersão sobre o Estado do Rio de Janeiro é também testada em um segundo experimento.

Real neurons, and their networks, are far too complex to be described exactly by simple deterministic equations. Any description of their dynamics must therefore incorporate noise to some degree. It is my thesis that the nervous system is organized in such a way that its performance is optimal, subject to this constraint. I further contend that neuronal dynamics may even be enhanced by noise, when compared with their deterministic counter-parts. To support my thesis I will present and analyze three case studies. I will show how noise might (i) extend the dynamic range of mammalian cold-receptors and other cells that exhibit a temperature-dependent discharge; (ii) feature in the perception of ambiguous figures such as the Necker cube; (iii) alter the discharge pattern of single cells.

Ziel der vorliegenden Arbeit ist es, das Preisach-Modell bezüglich stochastischer äußerer Felder und temperaturbezogener Aspekte zu untersuchen. Das phänomenologische Preisach-Modell wird oft erfolgreich angewendet, um Systeme mit Hysterese zu beschreiben. Im ersten Teil der Arbeit wird die Antwort des Preisach-Modells auf stochastische äußere Felder untersucht. Hier liegt das Augenmerk hauptsächlich auf der Autokorrelation; sie dient dazu den Einfluss des hysteretischen Gedächtnisses zu quantifizieren. Mit analytischen Methoden wird gezeigt, dass sich ein Langzeitgedächtnis, sichtbar in der Autokorrelation der Systemantwort, entwickeln kann, selbst wenn das treibende Feld unkorreliert ist. Im Anschluss werden diese Resultate, m.H. von Simulationen, auf äußere Felder ausgeweitet, die selbst Korrelationen aufweisen können. Der zweite Teil der Arbeit befasst sich mit dem Einfluss einer endlichen Temperatur auf das Preisach-Modell. Es werden unterschiedliche Methoden besprochen, wie das Nichtgleichgewichtsmodell in seiner mikromagnetischen Interpretation mit Temperatur als Gleichgewichtseigenschaft verknüpft werden kann. Eine Formulierung wird genutzt, um die Magnetisierung von Nickelnanopartikeln in einer Fullerenmatrix zu simulieren und mit Experimenten zu vergleichen. Des Weiteren wird die Relaxationsdynamik des Gedächtnisses des Preisach-Modells bei endlichen Temperaturen untersucht
The aim of this thesis is to investigate the Preisach model in regard to stochastically driving and temperature-related aspects. The Preisach model is a phenomenological model for systems with hysteresis which is often successfully applied. Hysteresis is a widespread phenomenon which is observed in nature and the key feature of certain technological applications. Further, it contributes to phenomena of interest in social science and economics as well. Prominent examples are the magnetization of ferromagnetic materials in an external magnetic field or the adsorption-desorption hysteresis observed in porous media. Hysteresis involves the development of a hysteresis memory, and multistability in the interrelations between external driving fields and system response. In the first part, we mainly investigate the response of Preisach hysteresis models driven by stochastic input processes with regard to autocorrelation functions to quantify the influence of the system’s memory. Using rigorous methods, it is shown that the development of a hysteresis memory is reflected in the possibility of long-time tails in the autocorrelation functions, even for uncorrelated driving fields. In the case of uncorrelated driving, these long-time tails in the autocorrelations of the system’s response are determined only by the tails of the involved densities. They will be observed if there are broad Preisach densities assigning a high weight to elementary loops of large width and narrow input densities such that rare extreme events of the input time series contribute significantly to the output for a long period of time. Afterwards, these results are extended by simulations to driving fields which themselves show correlations. It is shown that the autocorrelation of the output does not decay faster than the autocorrelation of the input process. Further, there is a possibility that long-term memory in the hysteretic response is more pronounced in the case of uncorrelated driving than in the case of correlated driving. The behavior of the output probability distribution at the saturation values is quite universal. It is not affected by the presence of correlations and allows conclusions whether the input density is much more narrow than the Preisach density or not. Moreover, the existence of effective Preisach densities is shown which define equivalence classes of systems of input and Preisach densities which lead to realizations of the same output variable. The asymptotic behavior of an effective Preisach density determines the asymptotic correlation decay of the system’s response in the case of uncorrelated driving. In the second part, temperature-related effects are considered. It is reviewed how the non-equilibrium Preisach model in its micromagnetic picture can be related to temperature within the framework of extended irreversible thermodynamics. The irreversible response of a ferromagnetic material, namely, Nickel nanoparticles in a fullerene matrix, is simulated. The model includes superparamagnetism where ferromagnetism breaks down at temperatures lower than the Curie temperature and the results are compared to experimental data. Furthermore, we adapt known results for the thermal relaxation of the system’s memory in the form of a front propagation in the Preisach plane derived basically from solving a master equation and by the use of a contradictory assumption. A closer look is taken at short time scales which dissolves the contradiction and shows that the known results apply, taking into account the fact that the dividing line propagation starts with an additional delay time depending on the front coordinates in the Preisach plane. Additionally, it is outlined how thermal relaxation behavior in the Preisach model of hysteresis can be studied using a Fokker-Planck equation. The latter is solved analytically in the non-hysteretic limit using eigenfunction methods. The results indicate a change in the relaxation behavior, especially on short time scales

This doctoral study consists of two parts. In the first part, the quantum statistical effects on the formation process of the heavy ion fusion reactions have been investigated by using the c-number quantum Langevin equation approach. It has been shown that the quantum effects enhance the over-passing probability at low temperatures. In the second part, we have developed a simulation technique for the quantum noises which can be approximated by two-term exponential colored noise.

Stochastic models describing how interacting individuals give rise to collective behaviour have become a widely used tool across disciplines—ranging from biology to physics to social sciences. Continuum population-level models based on partial differential equations for the population density can be a very useful tool (when, for large systems, particle-based models become computationally intractable), but the challenge is to predict the correct macroscopic description of the key attributes at the particle level (such as interactions between individuals and evolution rules). In this thesis we consider the simple class of models consisting of diffusive particles with short-range interactions. It is relevant to many applications, such as colloidal systems and granular gases, and also for more complex systems such as diffusion through ion channels, biological cell populations and animal swarms. To derive the macroscopic model of such systems, previous studies have used ad hoc closure approximations, often generating errors. Instead, we provide a new systematic method based on matched asymptotic expansions to establish the link between the individual- and the population-level models. We begin by deriving the population-level model of a system of identical Brownian hard spheres. The result is a nonlinear diffusion equation for the one-particle density function with excluded-volume effects enhancing the overall collective diffusion rate. We then expand this core problem in several directions. First, for a system with two types of particles (two species) we obtain a nonlinear cross-diffusion model. This model captures both alternative notions of diffusion, the collective diffusion and the self-diffusion, and can be used to study diffusion through obstacles. Second, we study the diffusion of finite-size particles through confined domains such as a narrow channel or a Hele–Shaw cell. In this case the macroscopic model depends on a confinement parameter and interpolates between severe confinement (e.g., a single- file diffusion in the narrow channel case) and an unconfined situation. Finally, the analysis for diffusive soft spheres, particles with soft-core repulsive potentials, yields an interaction-dependent non-linear term in the diffusion equation.

Centro Latino-Americano de Física
Os processos estocásticos com ruído branco multiplicativo são objeto de atenção constante em uma grande área da pesquisa científica. A variedade de prescrições possíveis para definir matematicamente estes processos oferece um obstáculo ao desenvolvimento de ferramentas gerais para seu tratamento. Na presente tese, estudamos propriedades de equilíbrio de processos markovianos com ruído branco multiplicativo. Para conseguirmos isto, definimos uma transformação de reversão temporal de tais processos levando em conta que a distribuição estacionária de probabilidade depende da prescrição. Deduzimos um formalismo funcional visando obter o funcional gerador das funções de correlação e resposta de um processo estocástico multiplicativo representado por uma equação de Langevin. Ao representar o processo estocástico neste formalismo (de Grassmann) funcional eludimos a necessidade de fixar uma prescrição particular. Neste contexto, analisamos as propriedades de equilíbrio e estudamos as simetrias ocultas do processo. Mostramos que, usando uma definição apropriada da distribuição de equilíbrio e considerando a transformação de reversão temporal adequada, as propriedades usuais de equilíbrio são satisfeitas para qualquer prescrição. Finalmente, apresentamos uma dedução detalhada da formulação supersimétrica covariante de um processo markoviano com ruído branco multiplicativo e estudamos algumas das relações impostas pelas funções de correlação através das identidades de Ward-Takahashi.
Multiplicativewhite-noise stochastic processes continuously attract the attention of a wide area of scientific research. The variety of prescriptions available to define it difficults the development of general tools for its characterization. In this thesis, we study equilibrium properties of Markovian multiplicative white-noise processes. For this, we define the time reversal transformation for this kind of processes, taking into account that the asymptotic stationary probability distribution depends on the prescription. We deduce a functional formalism to derive a generating functional for correlation and response functions of a multiplicative stochastic process represented by a Langevin equation. Representing the stochastic process in this functional (Grassmann) formalism, we avoid the necessity of fixing a particular prescription. In this framework, we analyze equilibrium properties and study hidden symmetries of the process. We show that, using a careful definition of equilibrium distribution and taking into account the appropriate time reversal transformation, usual equilibrium properties are satisfied for any prescription. Finally, we present a detailed deduction of a covariant supersymmetric formulation of a multiplicativeMarkovian white-noise process and study some of the constraints it imposes on correlation functions using Ward-Takahashi identities.

Quantification of stochastic or quantum systems by a joint probability density or wave function is a notoriously difficult computational problem, since the solution depends on all possible states (or realizations) of the system. Due to this combinatorial flavor, even a system containing as few as ten particles may yield as many as $10^{10}$ discretized states. None of even modern supercomputers are capable to cope with this curse of dimensionality straightforwardly, when the amount of quantum particles, for example, grows up to more or less interesting order of hundreds. A traditional approach for a long time was to avoid models formulated in terms of probabilistic functions, and simulate particular system realizations in a randomized process. Since different times in different communities, data-sparse methods came into play. Generally, they aim to define all data points indirectly, by a map from a low amount of representers, and recast all operations (e.g. linear system solution) from the initial data to the effective parameters. The most advanced techniques can be applied (at least, tried) to any given array, and do not rely explicitly on its origin. The current work contributes further progress to this area in the particular direction: tensor product methods for separation of variables. The separation of variables has a long history, and is based on the following elementary concept: a function of many variables may be expanded as a product of univariate functions. On the discrete level, a function is encoded by an array of its values, or a tensor. Therefore, instead of a huge initial array, the separation of variables allows to work with univariate factors with much less efforts. The dissertation contains a short overview of existing tensor representations: canonical PARAFAC, Hierarchical Tucker, Tensor Train (TT) formats, as well as the artificial tensorisation, resulting in the Quantized Tensor Train (QTT) approximation method. The contribution of the dissertation consists in both theoretical constructions and practical numerical algorithms for high-dimensional models, illustrated on the examples of the Fokker-Planck and the chemical master equations. Both arise from stochastic dynamical processes in multiconfigurational systems, and govern the evolution of the probability function in time. A special focus is put on time propagation schemes and their properties related to tensor product methods. We show that these applications yield large-scale systems of linear equations, and prove analytical separable representations of the involved functions and operators. We propose a new combined tensor format (QTT-Tucker), which descends from the TT format (hence TT algorithms may be generalized smoothly), but provides complexity reduction by an order of magnitude. We develop a robust iterative solution algorithm, constituting most advantageous properties of the classical iterative methods from numerical analysis and alternating density matrix renormalization group (DMRG) techniques from quantum physics. Numerical experiments confirm that the new method is preferable to DMRG algorithms. It is as fast as the simplest alternating schemes, but as reliable and accurate as the Krylov methods in linear algebra.

Cette thèse traite du comportement en temps long des équations stochastiques de Fokker-Planck en présence d’un bruit commun additif et présente des méthodes statistiques pour estimer la mesure invariante des processus de diffusion ergodiques multidimensionnels à partir de données bruitées. Dans la première partie, nous analysons les équations différentielles partielles stochastiques de type Fokker-Planck non linéaires, obtenues comme la limite du champ moyen de systèmes de particules en interaction dirigés par des bruits browniens idiosyncrasiques et en présence de bruit commun. Nous établissons des conditions sous lesquelles l’ajout d’un bruit commun promet de restaurer l’unicité de la mesure invariante. La principale difficulté provient de la dimension finie du bruit commun, alors que la variable d’état- interprétée comme la loi marginale conditionnelle du système compte tenu du bruit commun - opère dans un espace de dimension infinie. Nous démontrons que l’unicité est rétablie dès lors que le terme d’interaction du champ moyen attire le système vers sa moyenne conditionnelle (par rapport au bruit commun), en particulier lorsque l’intensité du bruit idiosyncrasique est faible, qui sont des cas typiques de perte d’unicité en l’absence de bruit commun. Dans la deuxième partie, nous développons une méthodologie statistique afin d’approximer la mesure invariante d’un processus de diffusion à partir d’observations bruitées et discrètes de ce même processus. Cette méthode implique une technique de pré-moyennage des données qui réduit l’intensité du bruit tout en conservant les caractéristiques analytiques et les propriétés asymptotiques du signal sous-jacent. Nous étudions le taux de convergence de cet estimateur, qui dépend de la régularité anisotrope de la densité et de l’intensité du bruit. Nous établissons ensuite des conditions sur l’intensité du bruit qui permettent d’obtenir des taux de convergence comparables à ceux des cas sans bruit. Enfin, nous démontrons une inégalité de concentration de type Bernstein pour notre estimateur, ce qui promet de mettre en place une procédure adaptative pour la sélection de la fenêtre du noyau
This thesis deals with the long-time behavior of stochastic Fokker-Planck equations with additive common noise and presents statistical methods for estimating the invariant measure of multidimensional ergodic diffusion processes from noisy data. In the first part, we analyze stochastic Fokker-Planck Partial Differential Equations (SPDEs), obtained as the mean-field limit of interacting particle systems influenced by both idiosyncratic and common Brownian noises. We establish conditions under which the addition of common noise restores uniqueness if the invariant measure. The main challenge arises from the finite-dimensional nature of the common noise, while the state variable — interpreted as the conditional marginal law of the system given the common noise — operates within an infinite-dimensional space. We demonstrate that uniqueness is restored if the mean field interaction term attracts the system towards its conditional mean given the common noise, particularly when the intensity of the idiosyncratic noise is small. In the second part, we develop a new statistical methodology using kernel density estimation to effectively approximate the invariant measure from noisy observations, highlighting the crucial role of the underlying Markov structure in the denoising process. This method involves a pre-averaging technique that proficiently reduces the intensity of the noise while maintaining the analytical characteristics and asymptotic properties of the underlying signal. We investigate the convergence rate of our estimator, which depends on the anisotropic regularity of the density and the intensity of the noise. We establish noise intensity conditions that allow for convergence rates comparable to those in noise-free environments. Additionally, we demonstrate a Bernstein concentration inequality for our estimator, leading to an adaptive procedure for selecting the kernel bandwidth

Nella fase di design e simulazione di un acceleratore circolare, risulta fondamentale avere una buona comprensione del moto betatronico non-lineare delle singole particelle, e di come questo interferisce con la qualità del fascio. In questo lavoro vengono studiati sistemi Hamiltoniani sottoposti a perturbazioni stocastiche tramite un framework diffusivo, basato sull'equazione di Fokker-Planck. Tale studio viene poi applicato all'analisi del moto betatronico non-lineare e al problema dell'Apertura Dinamica. In particolare, vengono impostate le basi per formulare un metodo di interpolazione di processi diffusivi simil-Nekhoroshev e viene proposta una procedura sperimentale per misurare gli effetti di diffusione locale all'interno di un acceleratore. L'Apertura Dinamica è una quantità chiave per il comportamento a lungo termine di un acceleratore, tuttavia, la misura di questa quantità nelle simulazioni presenta serie difficoltà dal punto di vista computazionale. È dunque nel nostro interesse riuscire a formulare una legge che descriva la dipendenza dal tempo dell'Apertura Dinamica.

This thesis considers two areas of research in non-equilibrium soft matter at the mesoscale. In the first part we introduce active colloids in the context of active matter and focus on the particular case of phoretic colloids. The general theory of phoresis is presented along with an expression for the phoretic velocity of a colloid and its rotational diffusion in two and three dimensions. We introduce a model for thermally active colloids that absorb light and emit heat and propel through thermophoresis. Using this model we develop the equations of motion for their collective dynamics and consider excluded volume through a lattice gas formalism. Solutions to the thermoattractive collective dynamics are studied in one dimension analytically and numerically. A few numerical results are presented for the collective dynamics in two dimensions. We simulate an unconfined system of thermally active colloids under directed illumination with simple projection based geometric optics. This system self-organises into a comet-like swarm and exhibits a wide range of non- equilibrium phenomena. In the second part we review the background of polymer translocation, including key experiments, theoretical progress and simulation studies. We present, discuss and use a common model to investigate the potential of patterned nanopores for stochastic sensing and identification of polynucleotides and other heteropolymers. Three pore patterns are characterised in terms of the response of a homopolymer with varying attractive affinity. This is extended to simple periodic block co-polymer heterostructures and a model device is proposed and demonstrated with two stochastic sensing algorithms. We find that mul- tiple sequential measurements of the translocation time is sufficient for identification with high accuracy. Motivated by fluctuating biological channels and the prospect of frequency based selectivity we investigate the response of a homopolymer through a pore that has a time dependent geometry. We show that a time dependent mobility can capture many features of the frequency response.

Cette thèse présente quelques résultats dans le domaine des équations aux dérivées partielles stochastiques. Une majeure partie d'entre eux concerne l'étude de limites diffusives de modèles cinétiques perturbés par un terme aléatoire. On présente également un résultat de régularité pour une classe d'équations aux dérivées partielles stochastiques ainsi qu'un résultat d'existence et d'unicité de mesures invariantes pour une équation de Fokker-Planck stochastique. Dans un premier temps, on présente trois travaux d'approximation-diffusion dans le contexte stochastique. Le premier s'intéresse au cas d'une équation cinétique avec opérateur de relaxation linéaire dont l'équilibre des vitesses a un comportement de type puissance à l'infini. L'équation est perturbée par un processus Markovien. Cela donne lieu à une limite fluide stochastique fractionnaire. Les deux autres résultats concernent l'étude de l'équation de transfert radiatif qui est un problème cinétique non linéaire. L'équation est bruitée dans un premier temps avec un processus de Wiener cylindrique et dans un second temps par un processus Markovien. Dans les deux cas, on obtient à la limite une équation de Rosseland stochastique. Dans la suite, on présente un résultat de régularité pour les équations aux dérivées partielles quasi-linéaires de type parabolique dont la partie aléatoire est gouvernée par un processus de Wiener cylindrique. Enfin, on étudie une équation de Fokker-Planck qui présente un terme de forçage aléatoire régi par un processus de Wiener cylindrique. On prouve d'une part l'existence et l'unicité des solutions de ce problème et d'autre part l'existence et l'unicité de mesures invariantes pour la dynamique de cette équation
This thesis presents several results about stochastic partial differential equations. The main subject is the study of diffusive limits of kinetic models perturbed with a random term. We also present a result about the regularity of a class of stochastic partial differential equations and a result of existence and uniqueness of invariant measures for a stochastic Fokker-Planck equation.First, we give three results of approximation-diffusion in a stochastic context. The first one deals with the case of a kinetic equation with a linear operator of relaxation whose velocity equilibrium has a power tail distribution at ininity. The equation is perturbed with a Markovian process. This gives rise to a stochastic fluid fractional limit. The two remaining results consider the case of the radiative transfer equation which is a non-linear kinetic equation. The equation is perturbed successively with a cylindrical Wiener process and with a Markovian process. In both cases, we are led to a stochastic Rosseland fluid limit.Then, we introduce a result of regularity for a class of quasilinear stochastic partial differential equations of parabolic type whose random term is driven by a cylindrical Wiener process.Finally, we study a Fokker-Planck equation with a noisy force governed by a cylindrical Wiener process. We prove existence and uniqueness of solutions to the problem and then existence and uniqueness of invariant measures to the equation

This thesis concerns modelling of Coulomb collisions in toroidal plasma with Monte Carlo operators, which is important for many applications such as heating, current drive and collisional transport in fusion plasmas. Collisions relax the distribution functions towards local isotropic ones and transfer power to the background species when they are perturbed e.g. by wave-particle interactions or injected beams. The evolution of the distribution function in phase space, due to the Coulomb scattering on background ions and electrons and the interaction with RF waves, can be obtained by solving a Fokker-Planck equation.The coupling between spatial and velocity coordinates in toroidal plasmas correlates the spatial diffusion with the pitch angle scattering by Coulomb collisions. In many applications the diffusion coefficients go to zero at the boundaries or in a part of the domain, which makes the SDE singular. To solve such SDEs or equivalent diffusion equations with Monte Carlo methods, we have proposed a new method, the hybrid method, as well as an adaptive method, which selects locally the faster method from the drift and diffusion coefficients. The proposed methods significantly reduce the computational efforts and improves the convergence. The radial diffusion changes rapidly when crossing the trapped-passing boundary creating a boundary layer. To solve this problem two methods are proposed. The first one is to use a non-standard drift term in the Monte Carlo equation. The second is to symmetrize the flux across the trapped passing boundary. Because of the coupling between the spatial and velocity coordinates drift terms associated with radial gradients in density, temperature and fraction of the trapped particles appear. In addition an extra drift term has been included to relax the density profile to a prescribed one. A simplified RF-operator in combination with the collision operator has been used to study the relaxation of a heated distribution function. Due to RF-heating the density of thermal ions is reduced by the formation of a high-energy tail in the distribution function. The Coulomb collisions tries to restore the density profile and thus generates an inward diffusion of thermal ions that results in a peaking of the total density profile of resonant ions.

QC 20130415

Cette thèse s'intéresse aux équations différentielles stochastiques de type McKean(EDS) et à leur utilisation pour représenter des équations aux dérivées partielles (EDP) non linéaires. Ces équations ne dépendent pas seulement du temps et de la position d'une certaine particule mais également de sa loi. En particulier nous traitons le cas inhabituel de la représentation d'EDP de type Fokker-Planck avec condition terminale fixée. Nous discutons existence et unicité pour ces EDP et de leur représentation sous la forme d'une EDS de type McKean, dont l'unique solutioncorrespond à la dynamique du retourné dans le temps d'un processus de diffusion.Nous introduisons la notion de représentation complètement non-linéaire d'une EDP semilinéaire. Celle-ci consiste dans le couplage d'une EDS rétrograde et d'un processus solution d'une EDS évoluant de manière rétrograde dans le temps. Nous discutons également une application à la représentation d'une équation d'Hamilton-Jacobi-Bellman (HJB) en contrôle stochastique. Sur cette base, nous proposonsun algorithme de Monte-Carlo pour résoudre des problèmes de contrôle. Celui ciest avantageux en termes d'efficience calculatoire et de mémoire, en comparaisonavec les approches traditionnelles progressive rétrograde. Nous appliquons cette méthode dans le contexte de la gestion de la demande dans les réseaux électriques. Pour finir, nous faisons le point sur l'utilisation d'EDS de type McKean généralisées pour représenter des EDP non-linéaires et non-conservatives plus générales que Fokker-Planck
This thesis concerns McKean Stochastic Differential Equations (SDEs) to representpossibly non-linear Partial Differential Equations (PDEs). Those depend not onlyon the time and position of a given particle, but also on its probability law. In particular, we treat the unusual case of Fokker-Planck type PDEs with prescribed final data. We discuss existence and uniqueness for those equations and provide a probabilistic representation in the form of McKean type equation, whose unique solution corresponds to the time-reversal dynamics of a diffusion process.We introduce the notion of fully backward representation of a semilinear PDE: thatconsists in fact in the coupling of a classical Backward SDE with an underlying processevolving backwardly in time. We also discuss an application to the representationof Hamilton-Jacobi-Bellman Equation (HJB) in stochastic control. Based on this, we propose a Monte-Carlo algorithm to solve some control problems which has advantages in terms of computational efficiency and memory whencompared to traditional forward-backward approaches. We apply this method in the context of demand side management problems occurring in power systems. Finally, we survey the use of generalized McKean SDEs to represent non-linear and non-conservative extensions of Fokker-Planck type PDEs

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Lagrangian stochastic models are a largely used tool in the study of passive substances dispersion inside the Atmospheric Boundary Layer. Its application is related to the trajectory computation of thousands of particles, that numerically simulate the dispersion of suspense substances in the atmosphere. In this study, the basic concepts related to the Lagrangian stochastic modelling are presented and discussed together with its main characteristics and its computational implementation, to the study of particles dispersion in the atmosphere. In a computational experiment, the obtained results are compared with observational data from the TRACT experiment, that took place in Europe in 1992. The input data needed for the dispersion model are extracted from simulations with the numerical weather forecast model RAMS. Dispersion over Rio de Janeiro region is also tested in a second experiment.
Modelos Lagrangianos estocásticos constituem ferramenta muito utilizada no estudo da dispersão de substâncias passivas na Camada Limite Atmosférica. Sua aplicação consiste em calcular a trajetória de milhares de partículas, que simulam numericamente a dispersão de uma substância em suspensão na atmosfera. Nesta tese, são apresentados e discutidos os conceitos básicos relacionados à Modelagem Lagrangiana Estocástica de Partículas, bem como suas principais características e sua implementação computacional, para o estudo da dispersão de partículas na atmosfera. Numa experimentação computacional, comparam-se os resultados obtidos com dados observacionais provenientes do experimento TRACT, realizado na Europa em 1992. Os dados de entrada necessários ao modelo de dispersão são extraídos de simulações do modelo de previsão numérica do tempo RAMS. A dispersão sobre o Estado do Rio de Janeiro é também testada em um segundo experimento.

Much of the effort in the modern chemical and physical sciences is devoted to the study of complex dynamical phenomena. Such a study is often hampered by the considerable complexity (i.e., the high dimensionality) exhibited by the systems of interest. In this research project, of theoretical and methodological character, we explore some facets of the topics of model reduction and simplification of complex dynamics, both deterministic and stochastic. In particular, in the first part of the work (chs. 2-5), we focus on deterministic systems. In chapter 2, starting from the findings of two previous works [P. Nicolini and D. Frezzato, J. Chem. Phys. 138, 234101 (2013) and P. Nicolini and D. Frezzato, J. Chem. Phys. 138, 234102 (2013)] we introduce the concept of "canonical format" of the evolution law for mass-action-based chemical kinetics, and show that the study of such a type of formats could lead to the discovery of new interesting features and to a rationalization of already well-known ones. Specifically, we unveil the existence of "attracting subspaces" in an abstract "hyper-spherical" representation of the dynamics of a reacting system. In chapter 3, based on the theory devised in ch. 2, we develop an algorithm (implemented in the companion software DRIMAK, acronym of Dimensional Reduction for Isothermal Mass-Action Kinetics) aimed at detecting the neighborhood of the Slow Manifold, which is a hypersurface, in the concentration space, in the proximity of which the slow evolution takes place. The detection of the Slow Manifold for a reacting system is a potential key-step to elaborate dimensionality reduction strategies. In chapter 4 we extend the theory to open reaction networks, i.e., reaction networks with one or more reactants continuously injected in the reaction environment. Finally, in chapter 5 we further generalize the theory to general phase-space dynamics, possibly damped. The second part of the work (chs. 6-8) is devoted to stochastic systems. In chapter 6 we move the first steps towards the model reduction of stochastic chemical kinetics. Specifically, we show the existence of geometric structures (in the space of the number of molecules of each species) analogous to the Slow Manifold in the macroscopic counterpart. Still in the context of stochastic chemical kinetics, in chapter 7 we make a critical study of two common continuous approximations of the chemical master equation and of the associated Gillespie's stochastic simulation algorithm; namely, we investigate on the physical reliability of the chemical Fokker-Planck and chemical Langevin equations. In particular, we prove that both the approximations suffer from nonphysical probability currents at equilibrium, even for fully reversible and detailed-balanced chemical reaction networks. Finally, in chapter 8 we focus on general overdamped fluctuating systems, which, apart from very simple and low-dimensional cases, are often mathematically intractable. In this context, given the well-known difficulties for the mathematical treatment of such systems, we aim only at achieving a partial, but easily computable, information. Namely, we devise a set of mathematical time-dependent bounds for key-quantities describing the systems of interest.
Nelle moderne scienze fisiche e chimiche, uno sforzo considerevole è dedicato allo studio di fenomeni dinamici complessi. Tale studio è spesso ostacolato dalla considerevole complessità (dovuta all'elevata dimensionalità) dei sistemi di interesse. In questo progetto di ricerca, di carattere teorico e metodologico, esploriamo alcuni aspetti riguardanti la riduzione della dimensionalità e la semplificazione di dinamiche complesse, sia deterministiche che stocastiche. In particolare, la prima parte del lavoro (capitoli 2-5), si concentra su sistemi deterministici. Nel capitolo 2, partendo dai risultati ottenuti in due precedenti lavori [P. Nicolini and D. Frezzato, J. Chem. Phys. 138, 234101 (2013) and P. Nicolini and D. Frezzato, J. Chem. Phys. 138, 234102 (2013)] introduciamo il concetto di "forma canonica" della legge di evoluzione per cinetiche chimiche basate sulla legge di azione di massa, e mostriamo che lo studio di tali forme può condurre alla scoperta di nuove interessanti proprietà e alla razionalizzazione di altre già note. Specificamente, mostriamo l'esistenza di "sottospazi attrattivi" in una rappresentazione astratta (ipersferica) della dinamica del sistema reagente. Nel capitolo 3, basandoci sulla teoria formulata nel capitolo 2, sviluppiamo un algoritmo (implementato nel software DRIMAK, acronimo di Dimensional Reduction for Isothermal Mass-Action Kinetics) finalizzato alla localizzazione di punti prossimi allo Slow Manifold, ossia all’ipersuperficie, nello spazio delle concentrazioni, in prossimità della quale ha luogo la parte lenta dell'evoluzione. L'individuazione dello Slow Manifold per un sistema reagente è potenzialmente un passaggio chiave per elaborare strategie di riduzione di dimensionalità. Nel capitolo 4 estendiamo la teoria a network aperti di reazioni chimiche, ossia a casi in cui uno o più reagenti sono continuamente immessi nell'ambiente di reazione. Infine, nel capitolo 5 generalizziamo ulteriormente la teoria a dinamiche (anche smorzate) nello spazio delle fasi. La seconda parte del lavoro (capitoli 6-8) è dedicata ai sistemi stocastici. Nel capitolo 6 muoviamo i primi passi verso la riduzione di dimensionalità di cinetiche chimiche stocastiche. Specificamente, mostriamo l'esistenza di strutture geometriche (nello spazio dei numeri di molecole per ogni specie) analoghe agli Slow Manifold nella controparte macroscopica. Ancora nel contesto delle cinetiche chimiche stocastiche, nel capitolo 7 mostriamo i risultati di uno studio critico di due comuni approssimazioni continue della ‘chemical master equation’ e dell'algoritmo di simulazione di Gillespie, ossia, le cosiddette equazioni di Fokker-Planck e di Langevin “chimiche”. In particolare, dimostriamo che entrambe le approssimazioni soffrono di una inconsistenza fisica che si manifesta nella presenza di correnti di probabilità spurie all'equilibrio, anche per network di reazioni chimiche completamente reversibili e verificanti il bilancio dettagliato. Infine, nel capitolo 8 ci concentriamo su sistemi fluttuanti sovrasmorzati di tipo generale, i quali, a parte casi molto semplici e a bassa dimensionalità, sono spesso matematicamente intrattabili. In questo contesto miriamo ad ottenere solo un'informazione parziale, ma con basso costo computazionale, sullo stato futuro del sistema. In particolare, otteniamo una serie di disuguaglianze che consentono di vincolare alcune quantità rilevanti del sistema.

La thèse est consacrée à étudier les relations entre les Équations aux Derivées Partielles Stochastiques et l'operateur de Kolmogorov associé dans des espaces de fonctions continues.
Dans la première partie, la théorie de la convergence faibles des fonctions est mis au point afin de donner des résultats généraux sur les semi-groupes des Markov et leur générateur.
Dans la deuxième partie, des modèles de semi-groups de Markov associés à des équations aux dérivées partielles stochastiques sont étudiés. En particulier, Ornstein-Uhlenbeck, réaction-diffusion et équations de Burgers ont été envisagées. Pour chaque cas, le semi-groupe de transition et son générateur infinitésimal ont été étudiées dans un espace de fonctions continues.
Les résultats principaux montrent que l'ensemble des fonctions exponentielles fournit un Core pour l'opérateur de Kolmogorov. En conséquence, on prouve l'unicité de l'équation de Kolmogorov de mesures (autrement dit de Fokker-Planck).

Ziel dieser Arbeit ist die Untersuchung des Einflusses der stochastischen Interpretation der Langevin Gleichung mit zustandsabhängigen Diffusionskoeffizienten auf den Propagator des zugehörigen stochastischen Prozesses bzw. dessen Mittelwerte. Dies dient dem besseren Verständnis und der Interpretation von Messdaten von Diffusion in inhomogenen Systemen und geht einher mit der Frage der Form der Diffusionsgleichung in solchen Systemen. Zur Vereinfachung der Fragestellung werden in dieser Arbeit nur Systeme untersucht die vollständig durch einen ortsabhängigen Diffusionskoeffizienten und Angabe der stochastischen Interpretation beschrieben werden können. Dazu wird zunächst für mehrere experimentell relevante eindimensionale Systeme der jeweilige allgemeine Propagator bestimmt, der für jede denkbare stochastische Interpretation gültig ist. Der analytisch bestimmte Propagator wird dann für zwei exemplarisch ausgewählte stochastische Interpretationen, hier für die Itô und Klimontovich-Hänggi Interpretation, gegenübergestellt und die Unterschiede identifiziert. Für Mittelwert und Varianz der Prozesse werden die drei wesentlichen stochastischen Interpretationen verglichen, also Itô, Stratonovich und Klimontovich-Hänggi Interpretation. Diese systematische Untersuchung von inhomogenen Diffusionsprozessen kann zukünftig helfen diese Art von, in genau einer stochastischen Interpretation, driftfreien Systemen einfacher zu identifizieren. Ein weiterer wesentlicher Teil der Arbeit erweitert die Frage auf mehrdimensionale inhomogene anisotrope Systeme. Dies wird z.B. bei der Untersuchung von Diffusion in Flüssigkristallen mit inhomogenem Direktorfeld relevant. Obwohl hier, im Gegensatz zu eindimensionalen Systemen, der Propagator nicht allgemein berechnet werden kann, wird dennoch der Einfluss der Inhomogenität auf Messgrößen, wie die mittlere quadratische Verschiebung oder die Verteilung der Diffusivitäten, bestimmt. Anhand eines Beispiels wird auch der Einfluss der stochastischen Interpretation auf diese Messgrößen demonstriert
The aim of this thesis is to investigate the influence of the stochastic interpretation of the Langevin equation with state-dependent diffusion coefficient on the propagator of the related stochastic process, or its averages, respectively. This helps to obtain a deeper understanding and to interpret measurement data of diffusion in inhomogeneous systems and is accompanied with the question of the proper form of the diffusion equation in such systems. To simplify the question, in this thesis only systems are considered which can be fully described by a spatially dependent diffusion coefficient and a given stochastic interpretation. Therefore, for several experimentally relevant one-dimensional systems, the respective general propagator is determined, which is valid for any possible stochastic interpretation. Then, the propagator for two exemplary stochastic interpretations, here the Itô and Klimontovich-Hänggi interpretation, are compared and the differences are identified. For mean and variance of the processes three major interpretations are compared, namely the Itô, the Stratonovich and the Klimontovich-Hänggi interpretation. This systematic research on inhomogeneous diffusion process may help in future to identify these kind of, in exactly one stochastic interpretation, drift-free systems more easily. Another important part of this thesis extends this question to multidimensional inhomogeneous anisotropic systems. This is of high relevance, for instance, for the research of diffusion in liquid crystalline systems with an inhomogeneous director field. Although, in contrast to one-dimensional systems, the propagator may not be calculated generally, the influence of the inhomogeneity on measurement data like the mean squared displacement or the distribution of diffusivities is determined. Based on one example, also the influence of the stochastic interpretation on these quantities is demonstrated

The present habilitation thesis in theoretical biological physics addresses two central dynamical processes in cells and organisms: (i) active motility and motility control and (ii) self-organized pattern formation. The unifying theme is the nonlinear dynamics of biological function and its robustness in the presence of strong fluctuations, structural variations, and external perturbations. We theoretically investigate motility control at the cellular scale, using cilia and flagella as ideal model system. Cilia and flagella are highly conserved slender cell appendages that exhibit spontaneous bending waves. This flagellar beat represents a prime example of a chemo-mechanical oscillator, which is driven by the collective dynamics of molecular motors inside the flagellar axoneme. We study the nonlinear dynamics of flagellar swimming, steering, and synchronization, which encompasses shape control of the flagellar beat by chemical signals and mechanical forces. Mechanical forces can synchronize collections of flagella to beat at a common frequency, despite active motor noise that tends to randomize flagellar synchrony. In Chapter 2, we present a new physical mechanism for flagellar synchronization by mechanical self-stabilization that applies to free-swimming flagellated cells. This new mechanism is independent of direct hydrodynamic interactions between flagella. Comparison with experimental data provided by experimental collaboration partners in the laboratory of J. Howard (Yale, New Haven) confirmed our new mechanism in the model organism of the unicellular green alga Chlamydomonas. Further, we characterize the beating flagellum as a noisy oscillator. Using a minimal model of collective motor dynamics, we argue that measured non-equilibrium fluctuations of the flagellar beat result from stochastic motor dynamics at the molecular scale. Noise and mechanical coupling are antagonists for flagellar synchronization. In addition to the control of the flagellar beat by mechanical forces, we study the control of the flagellar beat by chemical signals in the context of sperm chemotaxis. We characterize a fundamental paradigm for navigation in external concentration gradients that relies on active swimming along helical paths. In this helical chemotaxis, the direction of a spatial concentration gradient becomes encoded in the phase of an oscillatory chemical signal. Helical chemotaxis represents a distinct gradient-sensing strategy, which is different from bacterial chemotaxis. Helical chemotaxis is employed, for example, by sperm cells from marine invertebrates with external fertilization. We present a theory of sensorimotor control, which combines hydrodynamic simulations of chiral flagellar swimming with a dynamic regulation of flagellar beat shape in response to chemical signals perceived by the cell. Our theory is compared to three-dimensional tracking experiments of sperm chemotaxis performed by the laboratory of U. B. Kaupp (CAESAR, Bonn). In addition to motility control, we investigate in Chapter 3 self-organized pattern formation in two selected biological systems at the cell and organism scale, respectively. On the cellular scale, we present a minimal physical mechanism for the spontaneous self-assembly of periodic cytoskeletal patterns, as observed in myofibrils in striated muscle cells. This minimal mechanism relies on the interplay of a passive coarsening process of crosslinked actin clusters and active cytoskeletal forces. This mechanism of cytoskeletal pattern formation exemplifies how local interactions can generate large-scale spatial order in active systems. On the organism scale, we present an extension of Turing’s framework for self-organized pattern formation that is capable of a proportionate scaling of steady-state patterns with system size. This new mechanism does not require any pre-pattering clues and can restore proportional patterns in regeneration scenarios. We analytically derive the hierarchy of steady-state patterns and analyze their stability and basins of attraction. We demonstrate that this scaling mechanism is structurally robust. Applications to the growth and regeneration dynamics in flatworms are discussed (experiments by J. Rink, MPI CBG, Dresden)
Das Thema der vorliegenden Habilitationsschrift in Theoretischer Biologischer Physik ist die nichtlineare Dynamik funktionaler biologischer Systeme und deren Robustheit gegenüber Fluktuationen und äußeren Störungen. Wir entwickeln hierzu theoretische Beschreibungen für zwei grundlegende biologische Prozesse: (i) die zell-autonome Kontrolle aktiver Bewegung, sowie (ii) selbstorganisierte Musterbildung in Zellen und Organismen. In Kapitel 2, untersuchen wir Bewegungskontrolle auf zellulärer Ebene am Modelsystem von Zilien und Geißeln. Spontane Biegewellen dieser dünnen Zellfortsätze ermöglichen es eukaryotischen Zellen, in einer Flüssigkeit zu schwimmen. Wir beschreiben einen neuen physikalischen Mechanismus für die Synchronisation zweier schlagender Geißeln, unabhängig von direkten hydrodynamischen Wechselwirkungen. Der Vergleich mit experimentellen Daten, zur Verfügung gestellt von unseren experimentellen Kooperationspartnern im Labor von J. Howard (Yale, New Haven), bestätigt diesen neuen Mechanismus im Modellorganismus der einzelligen Grünalge Chlamydomonas. Der Gegenspieler dieser Synchronisation durch mechanische Kopplung sind Fluktuationen. Wir bestimmen erstmals Nichtgleichgewichts-Fluktuationen des Geißel-Schlags direkt, wofür wir eine neue Analyse-Methode der Grenzzykel-Rekonstruktion entwickeln. Die von uns gemessenen Fluktuationen entstehen mutmaßlich durch die stochastische Dynamik molekularen Motoren im Innern der Geißeln, welche auch den Geißelschlag antreiben. Um die statistische Physik dieser Nichtgleichgewichts-Fluktuationen zu verstehen, entwickeln wir eine analytische Theorie der Fluktuationen in einem minimalen Modell kollektiver Motor-Dynamik. Zusätzlich zur Regulation des Geißelschlags durch mechanische Kräfte untersuchen wir dessen Regulation durch chemische Signale am Modell der Chemotaxis von Spermien-Zellen. Dabei charakterisieren wir einen grundlegenden Mechanismus für die Navigation in externen Konzentrationsgradienten. Dieser Mechanismus beruht auf dem aktiven Schwimmen entlang von Spiralbahnen, wodurch ein räumlicher Konzentrationsgradient in der Phase eines oszillierenden chemischen Signals kodiert wird. Dieser Chemotaxis-Mechanismus unterscheidet sich grundlegend vom bekannten Chemotaxis-Mechanismus von Bakterien. Wir entwickeln eine Theorie der senso-motorischen Steuerung des Geißelschlags während der Spermien-Chemotaxis. Vorhersagen dieser Theorie werden durch Experimente der Gruppe von U.B. Kaupp (CAESAR, Bonn) quantitativ bestätigt. In Kapitel 3, untersuchen wir selbstorganisierte Strukturbildung in zwei ausgewählten biologischen Systemen. Auf zellulärer Ebene schlagen wir einen einfachen physikalischen Mechanismus vor für die spontane Selbstorganisation von periodischen Zellskelett-Strukturen, wie sie sich z.B. in den Myofibrillen gestreifter Muskelzellen finden. Dieser Mechanismus zeigt exemplarisch auf, wie allein durch lokale Wechselwirkungen räumliche Ordnung auf größeren Längenskalen in einem Nichtgleichgewichtssystem entstehen kann. Auf der Ebene des Organismus stellen wir eine Erweiterung der Turingschen Theorie für selbstorganisierte Musterbildung vor. Wir beschreiben eine neue Klasse von Musterbildungssystemen, welche selbst-organisierte Muster erzeugt, die mit der Systemgröße skalieren. Dieser neue Mechanismus erfordert weder eine vorgegebene Kompartimentalisierung des Systems noch spezielle Randbedingungen. Insbesondere kann dieser Mechanismus proportionale Muster wiederherstellen, wenn Teile des Systems amputiert werden. Wir bestimmen analytisch die Hierarchie aller stationären Muster und analysieren deren Stabilität und Einzugsgebiete. Damit können wir zeigen, dass dieser Skalierungs-Mechanismus strukturell robust ist bezüglich Variationen von Parametern und sogar funktionalen Beziehungen zwischen dynamischen Variablen. Zusammen mit Kollaborationspartnern im Labor von J. Rink (MPI CBG, Dresden) diskutieren wir Anwendungen auf das Wachstum von Plattwürmern und deren Regeneration in Amputations-Experimenten

This dissertation addresses design and analysis aspects of stochastic dynamical systems using Fokker-Planck equation (FPE). A new numerical methodology based on the partition of unity meshless paradigm is developed to tackle the greatest hurdle in successful numerical solution of FPE, namely the curse of dimensionality. A local variational form of the Fokker-Planck operator is developed with provision for h- and p- refinement. The resulting high dimensional weak form integrals are evaluated using quasi Monte-Carlo techniques. Spectral analysis of the discretized Fokker- Planck operator, followed by spurious mode rejection is employed to construct a new semi-analytical algorithm to obtain near real-time approximations of transient FPE response of high dimensional nonlinear dynamical systems in terms of a reduced subset of admissible modes. Numerical evidence is provided showing that the curse of dimensionality associated with FPE is broken by the proposed technique, while providing problem size reduction of several orders of magnitude. In addition, a simple modification of norm in the variational formulation is shown to improve quality of approximation significantly while keeping the problem size fixed. Norm modification is also employed as part of a recursive methodology for tracking the optimal finite domain to solve FPE numerically. The basic tools developed to solve FPE are applied to solving problems in nonlinear stochastic optimal control and nonlinear filtering. A policy iteration algorithm for stochastic dynamical systems is implemented in which successive approximations of a forced backward Kolmogorov equation (BKE) is shown to converge to the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Several examples, including a four-state missile autopilot design for pitch control, are considered. Application of the FPE solver to nonlinear filtering is considered with special emphasis on situations involving long durations of propagation in between measurement updates, which is implemented as a weak form of the Bayes rule. A nonlinear filter is formulated that provides complete probabilistic state information conditioned on measurements. Examples with long propagation times are considered to demonstrate benefits of using the FPE based approach to filtering.

Yim Man Yi = 亞擴散雙穩分數福克-普朗克系統的線性響應及隨機共振和有色噪音對雙穩系統所引起的效應 / 嚴敏儀.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (leaves 85-89).
Text in English; abstracts in English and Chinese.
Yim Man Yi = Ya kuo san shuang wen fen shu Fuke-Pulangke xi tong de xian xing xiang ying ji sui ji gong zhen he you se zao yin dui shuang wen xi tong suo yin qi de xiao ying / Yan Minyi.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Brownian motion and anomalous dynamics --- p.1
Chapter 1.2 --- White and colored noises --- p.3
Chapter 2 --- Linear response theory of sub diffusive Fokker-Planck systems --- p.6
Chapter 2.1 --- Introduction to subdiffusive Fokker-Planck systems --- p.6
Chapter 2.2 --- Spectral density --- p.8
Chapter 2.3 --- Linear response --- p.11
Chapter 2.4 --- Signal-to-noise ratio (SNR) --- p.13
Chapter 2.5 --- Stochastic energy --- p.14
Chapter 3 --- Perturbation due to a sinusoidal signal --- p.16
Chapter 3.1 --- Presence of an external sinusoidal forcing --- p.16
Chapter 3.1.1 --- PDF and linear reponse --- p.16
Chapter 3.1.2 --- Phase lag --- p.19
Chapter 3.1.3 --- SNR --- p.19
Chapter 3.1.4 --- Stochastic energy --- p.23
Chapter 3.2 --- Presence of a sinusoidally time-varying diffusion coefficient --- p.23
Chapter 4 --- Perturbation due to a rectangular signal --- p.26
Chapter 4.1 --- Linear response to a rectangular pulse --- p.26
Chapter 4.2 --- Perturbation due to a periodic rectangular signal --- p.28
Chapter 4.2.1 --- Linear response --- p.29
Chapter 4.2.2 --- SNR --- p.31
Chapter 4.2.3 --- Stochastic energy --- p.33
Chapter 4.3 --- Comparison with the response to a sinusoidal driving force --- p.35
Chapter 5 --- The Effects of Colored Noise on the Stationary Probability Distribution --- p.38
Chapter 5.1 --- Formulation of a general colored noises-driven system --- p.39
Chapter 5.2 --- Approximation schemes for a colored noise --- p.42
Chapter 5.2.1 --- Decoupling approximation --- p.42
Chapter 5.2.2 --- UCNA --- p.45
Chapter 5.2.3 --- Small r approximation . --- p.46
Chapter 5.2.4 --- Presence of an additive noise: g(x) = 1 --- p.47
Chapter 5.2.5 --- Presence of a multiplicative noise: g(x) = x --- p.52
Chapter 5.3 --- Approximation scheme for two colored noises --- p.53
Chapter 5.3.1 --- Presence of two additive noises --- p.55
Chapter 5.3.2 --- Presence of an additive noise and a multiplicative noise --- p.55
Chapter 5.4 --- Unimodal-bimodal transitions --- p.61
Chapter 6 --- A stochastic genetic regulatory transcription model with colored noises --- p.68
Chapter 6.1 --- Biological background --- p.69
Chapter 6.2 --- Effect of a single noise --- p.72
Chapter 6.3 --- Effects of two noises --- p.75
Chapter 6.4 --- Biological implications of colored noises in the model --- p.81
Chapter 7 --- Conclusions --- p.82
Bibliography --- p.85
Chapter A --- Mittag-Leffler function --- p.90
Chapter B --- H-function (Fox function) --- p.92
Chapter C --- Operator algebra --- p.94
Chapter D --- Mean first passage time --- p.97

Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measures that have a non-reversible dynamic behavior. Practical applications include more powerful algorithms than the Metropolis algorithm to compute MRF models. In this paper we describe a large class of stochastic dynamical systems that has a Gibbs asymptotic distribution but does not satisfy reversibility. We characterize sufficient properties of a sub-class of stochastic differential equations in terms of the associated Fokker-Planck equation for the existence of an asymptotic probability distribution in the system of coordinates which is given. Practical implications include VLSI analog circuits to compute coupled MRF models.

In recent years, there has been increased awareness that stochasticity in chemical reactions and diffusion of molecules can have significant effects on the outcomes of intracellular processes, particularly given the low copy numbers of many proteins and mRNAs present in a cell. For such molecular species, the number and locations of molecules can provide a more accurate and detailed description than local concentration. In addition to diffusion, drift in the movements of molecules can play a key role in the dynamics of intracellular processes, and can often be modeled as arising from potential fields. Examples of sources of drift include active transport, variations in chemical potential, material heterogeneities in the cytoplasm, and local interactions with subcellular structures. This dissertation presents a new numerical method for simulating the stochastically varying numbers and locations of molecular species undergoing chemical reactions and drift-diffusion. The method combines elements of the First-Passage Kinetic Monte Carlo (FPKMC) method for reaction-diffusion systems and the Wang—Peskin—Elston lattice discretization of the Fokker—Planck equation that describes drift-diffusion processes in which the drift arises from potential fields. In the FPKMC method, each molecule is enclosed within a "protective domain," either by itself or with a small number of other molecules. To sample when a molecule leaves its protective domain or a reaction occurs, the original FPKMC method relies on analytic solutions of one- and two-body diffusion equations within the protective domains, and therefore cannot be used in situations with non-constant drift. To allow for such drift in our new method (hereafter Dynamic Lattice FPKMC or DL-FPKMC), each molecule undergoes a continuous-time random walk on a lattice within its protective domain, and the lattices change adaptively over time. One of the most commonly used spatial models for stochastic reaction-diffusion systems is the Smoluchowski diffusion-limited reaction (SDLR) model. The DL-FPKMC method generates convergent realizations of an extension of the SDLR model that includes drift from potentials. We present detailed numerical results demonstrating the convergence and accuracy of our method for various types of potentials (smooth, discontinuous, and constant). We also present several illustrative applications of DL-FPKMC, including examples motivated by cell biology.

Název práce: Stochastická dynamika bublin v DNA Autor: Bc. Vojtěch Kaiser Katedra: Katedra fyziky kondenzovaných látek Vedoucí diplomové práce: RNDr. Tomáš Novotný, Ph.D., Katedra fyziky kondenzovaných látek Abstrakt: Bubliny v DNA jsou místa, kde se vlivem tepelných či torsních vlivů otevírá dvojšroubovice DNA. Tyto bubliny jsou považovány za důležité pro termodynamiku DNA [56] a biologické procesy s DNA spojené [23,40,43,49]. V článcích [38, 39] byla řešena stochastická dynamika bublin v DNA na zá- kladě Polandova-Scheragova modelu a získány analytické výsledky při tep- lotě denaturace DNA a pro asymptotiku dlouhých časů, zvláště pro hustotu pravděpodobnosti času setkání konců bubliny. V této práci navazujeme na tyto výsledky a počítáme celkový tvar této hustoty pravděpodobností s vy- užitím numerické inverse analytických vztahů v Laplacově obraze. Dále po- čítáme hustotu pravděpodobnosti místa setkání konců bubliny. Odpovídající výsledky jsou numericky spočteny v případě molekul DNA konečné délky. Zachycování bubliny v oblastech bohatých na AT páry je modelováno jako subdifusivní systém dle článku [42] a jsou počítány stejné veličiny jako pro difusivní model. V závěru diskutujeme tyto výsledky a možnost jejich experi- mentálního ověření. Klíčová slova: bubliny v DNA,...

Ziel dieser Arbeit ist die Untersuchung des Einflusses der stochastischen Interpretation der Langevin Gleichung mit zustandsabhängigen Diffusionskoeffizienten auf den Propagator des zugehörigen stochastischen Prozesses bzw. dessen Mittelwerte. Dies dient dem besseren Verständnis und der Interpretation von Messdaten von Diffusion in inhomogenen Systemen und geht einher mit der Frage der Form der Diffusionsgleichung in solchen Systemen. Zur Vereinfachung der Fragestellung werden in dieser Arbeit nur Systeme untersucht die vollständig durch einen ortsabhängigen Diffusionskoeffizienten und Angabe der stochastischen Interpretation beschrieben werden können. Dazu wird zunächst für mehrere experimentell relevante eindimensionale Systeme der jeweilige allgemeine Propagator bestimmt, der für jede denkbare stochastische Interpretation gültig ist. Der analytisch bestimmte Propagator wird dann für zwei exemplarisch ausgewählte stochastische Interpretationen, hier für die Itô und Klimontovich-Hänggi Interpretation, gegenübergestellt und die Unterschiede identifiziert. Für Mittelwert und Varianz der Prozesse werden die drei wesentlichen stochastischen Interpretationen verglichen, also Itô, Stratonovich und Klimontovich-Hänggi Interpretation. Diese systematische Untersuchung von inhomogenen Diffusionsprozessen kann zukünftig helfen diese Art von, in genau einer stochastischen Interpretation, driftfreien Systemen einfacher zu identifizieren. Ein weiterer wesentlicher Teil der Arbeit erweitert die Frage auf mehrdimensionale inhomogene anisotrope Systeme. Dies wird z.B. bei der Untersuchung von Diffusion in Flüssigkristallen mit inhomogenem Direktorfeld relevant. Obwohl hier, im Gegensatz zu eindimensionalen Systemen, der Propagator nicht allgemein berechnet werden kann, wird dennoch der Einfluss der Inhomogenität auf Messgrößen, wie die mittlere quadratische Verschiebung oder die Verteilung der Diffusivitäten, bestimmt. Anhand eines Beispiels wird auch der Einfluss der stochastischen Interpretation auf diese Messgrößen demonstriert.
The aim of this thesis is to investigate the influence of the stochastic interpretation of the Langevin equation with state-dependent diffusion coefficient on the propagator of the related stochastic process, or its averages, respectively. This helps to obtain a deeper understanding and to interpret measurement data of diffusion in inhomogeneous systems and is accompanied with the question of the proper form of the diffusion equation in such systems. To simplify the question, in this thesis only systems are considered which can be fully described by a spatially dependent diffusion coefficient and a given stochastic interpretation. Therefore, for several experimentally relevant one-dimensional systems, the respective general propagator is determined, which is valid for any possible stochastic interpretation. Then, the propagator for two exemplary stochastic interpretations, here the Itô and Klimontovich-Hänggi interpretation, are compared and the differences are identified. For mean and variance of the processes three major interpretations are compared, namely the Itô, the Stratonovich and the Klimontovich-Hänggi interpretation. This systematic research on inhomogeneous diffusion process may help in future to identify these kind of, in exactly one stochastic interpretation, drift-free systems more easily. Another important part of this thesis extends this question to multidimensional inhomogeneous anisotropic systems. This is of high relevance, for instance, for the research of diffusion in liquid crystalline systems with an inhomogeneous director field. Although, in contrast to one-dimensional systems, the propagator may not be calculated generally, the influence of the inhomogeneity on measurement data like the mean squared displacement or the distribution of diffusivities is determined. Based on one example, also the influence of the stochastic interpretation on these quantities is demonstrated.

The present habilitation thesis in theoretical biological physics addresses two central dynamical processes in cells and organisms: (i) active motility and motility control and (ii) self-organized pattern formation. The unifying theme is the nonlinear dynamics of biological function and its robustness in the presence of strong fluctuations, structural variations, and external perturbations. We theoretically investigate motility control at the cellular scale, using cilia and flagella as ideal model system. Cilia and flagella are highly conserved slender cell appendages that exhibit spontaneous bending waves. This flagellar beat represents a prime example of a chemo-mechanical oscillator, which is driven by the collective dynamics of molecular motors inside the flagellar axoneme. We study the nonlinear dynamics of flagellar swimming, steering, and synchronization, which encompasses shape control of the flagellar beat by chemical signals and mechanical forces. Mechanical forces can synchronize collections of flagella to beat at a common frequency, despite active motor noise that tends to randomize flagellar synchrony. In Chapter 2, we present a new physical mechanism for flagellar synchronization by mechanical self-stabilization that applies to free-swimming flagellated cells. This new mechanism is independent of direct hydrodynamic interactions between flagella. Comparison with experimental data provided by experimental collaboration partners in the laboratory of J. Howard (Yale, New Haven) confirmed our new mechanism in the model organism of the unicellular green alga Chlamydomonas. Further, we characterize the beating flagellum as a noisy oscillator. Using a minimal model of collective motor dynamics, we argue that measured non-equilibrium fluctuations of the flagellar beat result from stochastic motor dynamics at the molecular scale. Noise and mechanical coupling are antagonists for flagellar synchronization. In addition to the control of the flagellar beat by mechanical forces, we study the control of the flagellar beat by chemical signals in the context of sperm chemotaxis. We characterize a fundamental paradigm for navigation in external concentration gradients that relies on active swimming along helical paths. In this helical chemotaxis, the direction of a spatial concentration gradient becomes encoded in the phase of an oscillatory chemical signal. Helical chemotaxis represents a distinct gradient-sensing strategy, which is different from bacterial chemotaxis. Helical chemotaxis is employed, for example, by sperm cells from marine invertebrates with external fertilization. We present a theory of sensorimotor control, which combines hydrodynamic simulations of chiral flagellar swimming with a dynamic regulation of flagellar beat shape in response to chemical signals perceived by the cell. Our theory is compared to three-dimensional tracking experiments of sperm chemotaxis performed by the laboratory of U. B. Kaupp (CAESAR, Bonn). In addition to motility control, we investigate in Chapter 3 self-organized pattern formation in two selected biological systems at the cell and organism scale, respectively. On the cellular scale, we present a minimal physical mechanism for the spontaneous self-assembly of periodic cytoskeletal patterns, as observed in myofibrils in striated muscle cells. This minimal mechanism relies on the interplay of a passive coarsening process of crosslinked actin clusters and active cytoskeletal forces. This mechanism of cytoskeletal pattern formation exemplifies how local interactions can generate large-scale spatial order in active systems. On the organism scale, we present an extension of Turing’s framework for self-organized pattern formation that is capable of a proportionate scaling of steady-state patterns with system size. This new mechanism does not require any pre-pattering clues and can restore proportional patterns in regeneration scenarios. We analytically derive the hierarchy of steady-state patterns and analyze their stability and basins of attraction. We demonstrate that this scaling mechanism is structurally robust. Applications to the growth and regeneration dynamics in flatworms are discussed (experiments by J. Rink, MPI CBG, Dresden).:1 Introduction 10 1.1 Overview of the thesis 10 1.2 What is biological physics? 12 1.3 Nonlinear dynamics and control 14 1.3.1 Mechanisms of cell motility 16 1.3.2 Self-organized pattern formation in cells and tissues 28 1.4 Fluctuations and biological robustness 34 1.4.1 Sources of fluctuations in biological systems 34 1.4.2 Example of stochastic dynamics: synchronization of noisy oscillators 36 1.4.3 Cellular navigation strategies reveal adaptation to noise 39 2 Selected publications: Cell motility and motility control 56 2.1 “Flagellar synchronization independent of hydrodynamic interactions” 56 2.2 “Cell body rocking is a dominant mechanism for flagellar synchronization” 57 2.3 “Active phase and amplitude fluctuations of the flagellar beat” 58 2.4 “Sperm navigation in 3D chemoattractant landscapes” 59 3 Selected publications: Self-organized pattern formation in cells and tissues 60 3.1 “Sarcomeric pattern formation by actin cluster coalescence” 60 3.2 “Scaling and regeneration of self-organized patterns” 61 4 Contribution of the author in collaborative publications 62 5 Eidesstattliche Versicherung 64 6 Appendix: Reprints of publications 66
Das Thema der vorliegenden Habilitationsschrift in Theoretischer Biologischer Physik ist die nichtlineare Dynamik funktionaler biologischer Systeme und deren Robustheit gegenüber Fluktuationen und äußeren Störungen. Wir entwickeln hierzu theoretische Beschreibungen für zwei grundlegende biologische Prozesse: (i) die zell-autonome Kontrolle aktiver Bewegung, sowie (ii) selbstorganisierte Musterbildung in Zellen und Organismen. In Kapitel 2, untersuchen wir Bewegungskontrolle auf zellulärer Ebene am Modelsystem von Zilien und Geißeln. Spontane Biegewellen dieser dünnen Zellfortsätze ermöglichen es eukaryotischen Zellen, in einer Flüssigkeit zu schwimmen. Wir beschreiben einen neuen physikalischen Mechanismus für die Synchronisation zweier schlagender Geißeln, unabhängig von direkten hydrodynamischen Wechselwirkungen. Der Vergleich mit experimentellen Daten, zur Verfügung gestellt von unseren experimentellen Kooperationspartnern im Labor von J. Howard (Yale, New Haven), bestätigt diesen neuen Mechanismus im Modellorganismus der einzelligen Grünalge Chlamydomonas. Der Gegenspieler dieser Synchronisation durch mechanische Kopplung sind Fluktuationen. Wir bestimmen erstmals Nichtgleichgewichts-Fluktuationen des Geißel-Schlags direkt, wofür wir eine neue Analyse-Methode der Grenzzykel-Rekonstruktion entwickeln. Die von uns gemessenen Fluktuationen entstehen mutmaßlich durch die stochastische Dynamik molekularen Motoren im Innern der Geißeln, welche auch den Geißelschlag antreiben. Um die statistische Physik dieser Nichtgleichgewichts-Fluktuationen zu verstehen, entwickeln wir eine analytische Theorie der Fluktuationen in einem minimalen Modell kollektiver Motor-Dynamik. Zusätzlich zur Regulation des Geißelschlags durch mechanische Kräfte untersuchen wir dessen Regulation durch chemische Signale am Modell der Chemotaxis von Spermien-Zellen. Dabei charakterisieren wir einen grundlegenden Mechanismus für die Navigation in externen Konzentrationsgradienten. Dieser Mechanismus beruht auf dem aktiven Schwimmen entlang von Spiralbahnen, wodurch ein räumlicher Konzentrationsgradient in der Phase eines oszillierenden chemischen Signals kodiert wird. Dieser Chemotaxis-Mechanismus unterscheidet sich grundlegend vom bekannten Chemotaxis-Mechanismus von Bakterien. Wir entwickeln eine Theorie der senso-motorischen Steuerung des Geißelschlags während der Spermien-Chemotaxis. Vorhersagen dieser Theorie werden durch Experimente der Gruppe von U.B. Kaupp (CAESAR, Bonn) quantitativ bestätigt. In Kapitel 3, untersuchen wir selbstorganisierte Strukturbildung in zwei ausgewählten biologischen Systemen. Auf zellulärer Ebene schlagen wir einen einfachen physikalischen Mechanismus vor für die spontane Selbstorganisation von periodischen Zellskelett-Strukturen, wie sie sich z.B. in den Myofibrillen gestreifter Muskelzellen finden. Dieser Mechanismus zeigt exemplarisch auf, wie allein durch lokale Wechselwirkungen räumliche Ordnung auf größeren Längenskalen in einem Nichtgleichgewichtssystem entstehen kann. Auf der Ebene des Organismus stellen wir eine Erweiterung der Turingschen Theorie für selbstorganisierte Musterbildung vor. Wir beschreiben eine neue Klasse von Musterbildungssystemen, welche selbst-organisierte Muster erzeugt, die mit der Systemgröße skalieren. Dieser neue Mechanismus erfordert weder eine vorgegebene Kompartimentalisierung des Systems noch spezielle Randbedingungen. Insbesondere kann dieser Mechanismus proportionale Muster wiederherstellen, wenn Teile des Systems amputiert werden. Wir bestimmen analytisch die Hierarchie aller stationären Muster und analysieren deren Stabilität und Einzugsgebiete. Damit können wir zeigen, dass dieser Skalierungs-Mechanismus strukturell robust ist bezüglich Variationen von Parametern und sogar funktionalen Beziehungen zwischen dynamischen Variablen. Zusammen mit Kollaborationspartnern im Labor von J. Rink (MPI CBG, Dresden) diskutieren wir Anwendungen auf das Wachstum von Plattwürmern und deren Regeneration in Amputations-Experimenten.:1 Introduction 10 1.1 Overview of the thesis 10 1.2 What is biological physics? 12 1.3 Nonlinear dynamics and control 14 1.3.1 Mechanisms of cell motility 16 1.3.2 Self-organized pattern formation in cells and tissues 28 1.4 Fluctuations and biological robustness 34 1.4.1 Sources of fluctuations in biological systems 34 1.4.2 Example of stochastic dynamics: synchronization of noisy oscillators 36 1.4.3 Cellular navigation strategies reveal adaptation to noise 39 2 Selected publications: Cell motility and motility control 56 2.1 “Flagellar synchronization independent of hydrodynamic interactions” 56 2.2 “Cell body rocking is a dominant mechanism for flagellar synchronization” 57 2.3 “Active phase and amplitude fluctuations of the flagellar beat” 58 2.4 “Sperm navigation in 3D chemoattractant landscapes” 59 3 Selected publications: Self-organized pattern formation in cells and tissues 60 3.1 “Sarcomeric pattern formation by actin cluster coalescence” 60 3.2 “Scaling and regeneration of self-organized patterns” 61 4 Contribution of the author in collaborative publications 62 5 Eidesstattliche Versicherung 64 6 Appendix: Reprints of publications 66

Tesis (Doctor en Física)--Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física, 2011.
Estudiamos el modelo de Potts de q estados bidimensional, que presenta transiciones de fase magnéticas con temperatura de primer (q > 4) y segundo orden (q = 4). Trabajamos con simulaciones tipo Monte Carlo para las cuales implementamos distintas técnicas algorítmicas, incluyendo una implementación en GPUs. No obstante, presentamos también algunos resultados analíticos. Analizamos la Dinámica de Tiempos Cortos en la aproximación de Campo Medio del modelo de Potts con q=2 resolviendo exactamente la ecuación de Fokker-Planck asociada a la dinámica de Glauber. Confirmamos la validez de la hipótesis de escala de la Dinámica de Tiempos Cortos tanto cerca del punto crítico como de puntos spinodales. Mostramos que es posible definir el punto spinodal a partir del comportamiento dinámico del sistema a tiempos cortos. Estudiamos la metaestabilidad asociada a la transición de fase de primer orden para el modelo de Potts de q estados con q > 4. Realizamos un estudio sistemático de la dinámica del modelo de Potts luego de un enfriamiento brusco a temperaturas subcríticas. Para q > 4 advertimos la existencia de diferentes regímenes dinámicos, de acuerdo al rango de temperaturas. Caracterizamos estos regímenes y los correspondientes estados del sistema.
We analyze the bidimensional q-state Potts model, a paradigmatic model in the study of Statistical Mechanics of Critical Phenomena and Phase Transitions, which presents first (q > 4) and second order (q ≤ 4) temperature driven magnetic phase transitions and has shown a very rich dynamic phenomenology. We mostly work on Monte Carlo numerical simulations, for which we have implemented different algorithm techniques, both traditional and original, including an implementation to run code on graphics cards. Nevertheless, we also present analytic results for some cases where this approach was possible. We study the Short Time Dynamics in the Mean-Field approximation for the 2-states Potts model (the Curie-Weiss model) solving the Fockker-Planck equation associated to the Glauber dynamics for this model. We obtain closed-form expressions for the first moments of the order parameter, near to both the critical and spinodal points, starting from different initial conditions. We confirm the validity of the short-time dynamical scaling hypothesis in both cases. We show that it is possible to define the spinodal point through the short time dynamical behaviour of the system; our definition works both for meanfield and short-range interactions systems. We study the the first order phase transition associated metastability for the q-state Potts model with q >4. We show that the spinodal point is clearly separated from the transition point for all q > 4, delimiting an interval of temperatures capable to hold metastable states. We provide numerical evidence for the existence of metastable states associated to the first order phase transition. We analyze the relaxation mechanism from these states to equilibrium. We perform a systematic study about the nonequilibrium dynamics of the Potts model on the square lattice after a quench from infinite to subcritical temperatures. We analyze the long term behaviour of the energy and relaxation time for a wide range of quench temperatures and system sizes. For q > 4 we found the existence of different dynamical regimes, according to quench temperature range. We characterize those regimes and the system’s corresponding states. We analyze in detail the finite size scaling properties of different relaxation times involved, as well as their temperature dependency.