Relevant bibliographies by topics / Computational stochastic dynamics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computational stochastic dynamics'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 September 2023

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Journal articles on the topic "Computational stochastic dynamics"

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Capiez-Lernout, E., and C. Soize. "Inverse problems in stochastic computational dynamics." Journal of Physics: Conference Series 135 (November 1, 2008): 012028. http://dx.doi.org/10.1088/1742-6596/135/1/012028.

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Johnson, E. A., C. Proppe, B. F. Spencer, L. A. Bergman, G. S. Székely, and G. I. Schuëller. "Parallel processing in computational stochastic dynamics." Probabilistic Engineering Mechanics 18, no. 1 (January 2003): 37–60. http://dx.doi.org/10.1016/s0266-8920(02)00041-3.

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Petromichelakis, Ioannis, and Ioannis A. Kougioumtzoglou. "Addressing the curse of dimensionality in stochastic dynamics: a Wiener path integral variational formulation with free boundaries." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2243 (November 2020): 20200385. http://dx.doi.org/10.1098/rspa.2020.0385.

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A Wiener path integral variational formulation with free boundaries is developed for determining the stochastic response of high-dimensional nonlinear dynamical systems in a computationally efficient manner. Specifically, a Wiener path integral representation of a marginal or lower-dimensional joint response probability density function is derived. Due to this a priori marginalization, the associated computational cost of the technique becomes independent of the degrees of freedom (d.f.) or stochastic dimensions of the system, and thus, the ‘curse of dimensionality’ in stochastic dynamics is circumvented. Two indicative numerical examples are considered for highlighting the capabilities of the technique. The first relates to marine engineering and pertains to a structure exposed to nonlinear flow-induced forces and subjected to non-white stochastic excitation. The second relates to nano-engineering and pertains to a 100-d.f. stochastically excited nonlinear dynamical system modelling the behaviour of large arrays of coupled nano-mechanical oscillators. Comparisons with pertinent Monte Carlo simulation data demonstrate the computational efficiency and accuracy of the developed technique.
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Breuer, H. P., and F. Petruccione. "A stochastic approach to computational fluid dynamics." Continuum Mechanics and Thermodynamics 4, no. 4 (1992): 247–67. http://dx.doi.org/10.1007/bf01129331.

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To, C. W. S. "On computational stochastic structural dynamics applying finite elements." Archives of Computational Methods in Engineering 8, no. 1 (March 2001): 3–40. http://dx.doi.org/10.1007/bf02736683.

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Holm, D. D., and V. Putkaradze. "Dynamics of non-holonomic systems with stochastic transport." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2209 (January 2018): 20170479. http://dx.doi.org/10.1098/rspa.2017.0479.

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This paper formulates a variational approach for treating observational uncertainty and/or computational model errors as stochastic transport in dynamical systems governed by action principles under non-holonomic constraints. For this purpose, we derive, analyse and numerically study the example of an unbalanced spherical ball rolling under gravity along a stochastic path. Our approach uses the Hamilton–Pontryagin variational principle, constrained by a stochastic rolling condition, which we show is equivalent to the corresponding stochastic Lagrange–d’Alembert principle. In the example of the rolling ball, the stochasticity represents uncertainty in the observation and/or error in the computational simulation of the angular velocity of rolling. The influence of the stochasticity on the deterministically conserved quantities is investigated both analytically and numerically. Our approach applies to a wide variety of stochastic, non-holonomically constrained systems, because it preserves the mathematical properties inherited from the variational principle.
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Bortolussi, Luca, and Alberto Policriti. "Hybrid Dynamics of Stochastic π-Calculus." Mathematics in Computer Science 2, no. 3 (March 2009): 465–91. http://dx.doi.org/10.1007/s11786-008-0065-3.

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Erban, Radek. "Coupling all-atom molecular dynamics simulations of ions in water with Brownian dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2186 (February 2016): 20150556. http://dx.doi.org/10.1098/rspa.2015.0556.

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Molecular dynamics (MD) simulations of ions (K + , Na + , Ca 2+ and Cl − ) in aqueous solutions are investigated. Water is described using the SPC/E model. A stochastic coarse-grained description for ion behaviour is presented and parametrized using MD simulations. It is given as a system of coupled stochastic and ordinary differential equations, describing the ion position, velocity and acceleration. The stochastic coarse-grained model provides an intermediate description between all-atom MD simulations and Brownian dynamics (BD) models. It is used to develop a multiscale method which uses all-atom MD simulations in parts of the computational domain and (less detailed) BD simulations in the remainder of the domain.
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Zhang, Libin, Zijun Yao, and Bin Wu. "Calculating biodiversity under stochastic evolutionary dynamics." Applied Mathematics and Computation 411 (December 2021): 126543. http://dx.doi.org/10.1016/j.amc.2021.126543.

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Tankhilevich, Evgeny, Jonathan Ish-Horowicz, Tara Hameed, Elisabeth Roesch, Istvan Kleijn, Michael P. H. Stumpf, and Fei He. "GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation." Bioinformatics 36, no. 10 (February 5, 2020): 3286–87. http://dx.doi.org/10.1093/bioinformatics/btaa078.

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Abstract Motivation Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and implementation https://github.com/tanhevg/GpABC.jl.
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Dissertations / Theses on the topic "Computational stochastic dynamics"

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Perez, Rafael A. "Uncertainty Analysis of Computational Fluid Dynamics Via Polynomial Chaos." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28984.

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The main limitations in performing uncertainty analysis of CFD models using conventional methods are associated with cost and effort. For these reasons, there is a need for the development and implementation of efficient stochastic CFD tools for performing uncertainty analysis. One of the main contributions of this research is the development and implementation of Intrusive and Non-Intrusive methods using polynomial chaos for uncertainty representation and propagation. In addition, a methodology was developed to address and quantify turbulence model uncertainty. In this methodology, a complex perturbation is applied to the incoming turbulence and closure coefficients of a turbulence model to obtain the sensitivity derivatives, which are used in concert with the polynomial chaos method for uncertainty propagation of the turbulence model outputs.
Ph. D.
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Breen, Barbara J. "Computational nonlinear dynamics monostable stochastic resonance and a bursting neuron model /." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180036/unrestricted/breen%5Fbarbara%5Fj%5F200312%5Fphd.pdf.

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Moix, Jeremy Michael. "Molecular Dynamics and Stochastic Simulations of Surface Diffusion." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14580.

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Despite numerous advances in experimental methodologies capable of addressing the various phenomenon occurring on metal surfaces, atomic scale resolution of the microscopic dynamics remains elusive for most systems. Computational models of the processes may serve as an alternative tool to fill this void. To this end, parallel molecular dynamics simulations of self-diffusion on metal surfaces have been developed and employed to address microscopic details of the system. However these simulations are not without their limitations and prove to be computationally impractical for a variety of chemically relevant systems, particularly for diffusive events occurring in the low temperature regime. To circumvent this difficulty, a corresponding coarse-grained representation of the surface is also developed resulting in a reduction of the required computational effort by several orders of magnitude, and this description becomes all the more advantageous with increasing system size and complexity. This representation provides a convenient framework to address fundamental aspects of diffusion in nonequilibrium environments and an interesting mechanism for directing diffusive motion along the surface is explored. In the ensuing discussion, additional topics including transition state theory in noisy systems and the construction of a checking function for protein structure validation are outlined. For decades the former has served as a cornerstone for estimates of chemical reaction rates. However, in complex environments transition state theory most always provides only an upper bound for the true rate. An alternative approach is described that may alleviate some of the difficulties associated with this problem. Finally, one of the grand challenges facing the computational sciences is to develop methods capable of reconstructing protein structure based solely on readily-available sequence information. Herein a checking function is developed that may prove useful for addressing whether a particular proposed structure is a viable possibility.
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Charlebois, Daniel. "Computational Investigations of Noise-mediated Cell Population Dynamics." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30339.

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Fluctuations, or "noise", can play a key role in determining the behaviour of living systems. The molecular-level fluctuations that occur in genetic networks are of particular importance. Here, noisy gene expression can result in genetically identical cells displaying significant variation in phenotype, even in identical environments. This variation can act as a basis for natural selection and provide a fitness benefit to cell populations under stress. This thesis focuses on the development of new conceptual knowledge about how gene expression noise and gene network topology influence drug resistance, as well as new simulation techniques to better understand cell population dynamics. Network topology may at first seem disconnected from expression noise, but genes in a network regulate each other through their expression products. The topology of a genetic network can thus amplify or attenuate noisy inputs from the environment and influence the expression characteristics of genes serving as outputs to the network. The main body of the thesis consists of five chapters: 1. A published review article on the physical basis of cellular individuality. 2. A published article presenting a novel method for simulating the dynamics of cell populations. 3. A chapter on modeling and simulating replicative aging and competition using an object-oriented framework. 4. A published research article establishing that noise in gene expression can facilitate adaptation and drug resistance independent of mutation. 5. An article submitted for publication demonstrating that gene network topology can affect the development of drug resistance. These chapters are preceded by a comprehensive introduction that covers essential concepts and theories relevant to the work presented.
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Martí, Ortega Daniel. "Neural stochastic dynamics of perceptual decision making." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7552.

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Models computacionals basats en xarxes a gran escala d'inspiració neurobiològica permeten descriure els correlats neurals de la decisió observats en certes àrees corticals com una transició entre atractors de la xarxa cortical. L'estimulació provoca un canvi en el paisatge d'atractors que afavoreix la transició entre l'atractor neutre inicial a un dels atractors associats a les eleccions categòriques. El soroll present en el sistema introdueix indeterminació en les transicions. En aquest treball mostrem l'existència de dos mecanismes de decisió qualitativament diferents, cadascun amb signatures psicofísiques diferenciades. El mecanisme que apareix a baixes intensitats, induït exclusivament pel soroll, dóna lloc a temps de decisió distribuïts asimètricament, amb una mitjana dictada per l'amplitud del soroll.A més, tant els temps de decisió com el rendiment psicofísic són funcions decreixents de l'estimulació externa. També proposem dos mètodes, un basat en l'aproximació macroscòpica i un altre en la teoria de la varietat central, que simplifiquen la descripció de sistemes estocàstics multistables.
Computational models based on large-scale, neurobiologically-inspired networks describe the decision-related activity observed in some cortical areas as a transition between attractors of the cortical network. Stimulation induces a change in the attractor configuration and drives the system out from its initial resting attractor to one of the existing attractors associated with the categorical choices. The noise present in the system renders transitions random. We show that there exist two qualitatively different mechanisms for decision, each with distinctive psychophysical signatures. The decision mechanism arising at low inputs, entirely driven by noise, leads to skewed distributions of decision times, with a mean governed by the amplitude of the noise. Moreover, both decision times and performances are monotonically decreasing functions of the overall external stimulation. We also propose two methods, one based on the macroscopic approximation and one based on center manifold theory, to simplify the description of multistable stochastic neural systems.
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Hannay, Jonathan David. "Computational simulations of thermally activated magnetisation dynamics at high frequencies." Thesis, Bangor University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367315.

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Dangerfield, C. E. "Stochastic models of ion channel dynamics and their role in short-term repolarisation variability in cardiac cells." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:cd0be850-1ff0-4792-8171-438ff8fc0161.

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Sudden cardiac death due to the development of lethal arrhythmias is the dominant cause of mortality in the UK, yet the mechanisms underlying their onset, maintenance and termination are still poorly understood. Therefore biomarkers are used to determine arrhythmic risk within patients and of new drug compounds. In recent years, the magnitude of variations in the length of successive beats, measured over a short period of time, has been shown to be a powerful predictor of arrhythmic risk. This beat-to-beat variability is thought to be the manifestation of the random opening and closing dynamics of individual ion channels that lie within the membrane of cardiac cells. Computational models have become an important tool in understanding the electrophysiology of the heart. However, current state-of-the-art electrophysiology models do not incorporate this intrinsic stochastic behaviour of ion channels. Those that do use computationally costly methods, restricting their use in complex tissue scale simulations, or employ stochastic simulation methods that result in negative numbers of channels and so are inaccurate. Therefore, using current stochastic modelling techniques to investigate the role of stochastic ion channel behaviour in beat-to-beat variability presents difficulties. In this thesis we take a mathematically rigorous and novel approach to develop accurate and computationally efficient models of stochastic ion channel dynamics that can be incorporated into existing electrophysiology models. Two different models of stochastic ion channel behaviour, both based on a system of stochastic differential equations (SDEs), are developed and compared. The first model is based on an existing SDE model from population dynamics called the Wright-Fisher model. The second approach incorporates boundary conditions into the SDE model of ion channel dynamics that is obtained in the limit from the discrete-state Markov chain model, and is called a reflected SDE. Of these two methods, the reflected SDE is found to more accurately capture the stochastic dynamics of the discrete-stateMarkov chain, seen as the ‘gold-standard’ model and also provides substantial computational speed up. Thus the reflected SDE is an accurate and efficient model of stochastic ion channel dynamics and so allows for detailed investigation into beat-to-beat variability using complex computational electrophysiology models. We illustrate the potential power of this method by incorporating it into a state-of-the-art canine cardiac cell electrophsyiology model so as to explore the effects of stochastic ion channel behaviour on beat-to-beat variability. The stochastic models presented in this thesis fulfil an important role in elucidating the effects of stochastic ion channel behaviour on beat-to-beat variability, a potentially important biomarker of arrhythmic risk.
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Infante, Gina Paola Polo. "Modeling and stochastic simulation to study the dynamics of Rickettsia rickettsii in populations of Hydrochoerus hydrochaeris and Amblyomma sculptum in the State of São Paulo, Brazil." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/10/10134/tde-19102017-154424/.

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There are a huge number of pathogens with multi-component transmission cycles, involving ampli_er hosts, vectors, complex pathogen life cycles and particular environmental conditions. These complex systems present challenges in terms of modeling and policy development. The deadliest tick-borne infectious disease in the world, the Brazilian Spotted Fever (BSF), is a relevant example of that. The current increase of human cases of BSF has been associated with the presence and expansion of capybaras Hydrochoerus hydrochaeris, amplifer host for the agent Rickettsia rickettsii and primary host for the tick vector Amblyomma sculptum. The objective of this thesis was to analyze the dynamics of the FMB with the purpose of providing bases for the planning of strategies focused on the prevention of human cases. We proposed diferent approaches to evaluating: i) the contribution of hosts and vectors in the transmission of BSF, ii) potential risk areas and anthropogenic parameters associated with the occurrence of human cases, iii) the pattern and the spatial propagation velocity of BSF, and iv) climatic and landscape factors that could be related to the distribution of the vector. The proposed approaches elucidated how BSF control and prevention strategies can be focused on the management of amplifier hosts populations. We found that geographical barriers generated, for example, by areas of riparian reforestation, could prevent the spatial spread of BSF, since a positive association between the occurrence of human cases and the increment of sugarcane crop was determined, as well as a higher propagation velocity of BSF in places with higher carrying capacity. This thesis was interdisciplinary and required, on one hand, expertise in biology, computational epidemiology, mathematics and statistics and on the other hand, a datarich environment such as the Laboratory of Parasitology of the VPS/FMVZ/USP. The results of this thesis can be usefulness in the planning of public health policies related to the prevention of BSF. Furthermore, this work will open the path to further mathematical and computational studies focused on the dynamics and prevention of other vector-borne infectious diseases.
Existe um grande número de agentes patogênicos com ciclos de transmissão complexos, envolvendo hospedeiros amplificadores, vetores e condições ambientais particulares. Esses sistemas complexos apresentam desafios quanto a modelagem e desenvolvimento de políticas públicas. A Febre Maculosa Brasileira (FMB) é a doença transmitida por carrapatos mais letal do mundo e é um claro exemplo de um sistema complexo. O aumento atual de casos humanos de BSF tem sido associado à presença e expansão de capivaras Hydrochoerus hydrochaeris, hospedeiros amplificadores do agente Rickettsia rickettsii e hospedeiros primários do carrapato vetor Amblyomma sculptum. O objetivo desta tese foi analisar a dinâmica da FMB com o propósito de fornecer bases para o delineamento de estratégias de prevenção de casos em humanos. Diferentes abordagens foram propostas para avaliar: i) a contribuição específica de hospedeiros e vetores na transmissão da FMB, ii) os parâmetros antropogênicos associados com a ocorrência dos casos e potenciais áreas de risco, iii) o padrão e a velocidade de propagação espacial e da doença, e iv) os fatores climáticos e paisagísticos que poderiam estar relacionados à distribuição do vetor. Os modelos propostos elucidaram que as estratégias de controle e prevenção da FMB podem estar focadas em práticas de manejo das populações de hospedeiros amplificadores. Uma vez que uma associação positiva entre ocorrência de casos humanos e o incremento de cultura de cana-de-açúcar foi determinada, assim como uma maior velocidade de propagação da FMB em locais com alta quantidade desta cultura, barreiras geográficas geradas, por exemplo, por zonas de reflorestamento ciliar, poderiam impedir a disseminação da FMB. Esta tese foi interdisciplinar e exigiu, por um lado, conhecimentos em biologia, epidemiologia computacional, matemática e estatística e, em contrapartida, um ambiente rico em dados biológicos como o Laboratório de Parasitologia do VPS/USP. Os resultados desta tese poderão ser utilizados na planificação de políticas de saúde pública enfocadas à prevenção da FMB. Complementarmente, este trabalho abrirá o caminho para futuros estudos matemáticos e computacionais orientados no estudo da dinâmica e prevenção de outras doenças infecciosas transmitidas por vetores.
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Tosi, Riccardo. "Towards stochastic methods in CFD for engineering applications." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/673389.

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Recent developments of high performance computing capabilities allow solving modern science problems employing sophisticated computational techniques. However, it is necessary to ensure the efficiency of state of the art computational methods to fully take advantage of modern technology capabilities. In this thesis we propose uncertainty quantification and high performance computing strategies to solve fluid dynamics systems characterized by uncertain conditions and unknown parameters. We verify that such techniques allow us to take decisions faster and ensure the reliability of simulation results. Different sources of uncertainties can be relevant in computational fluid dynamics applications. For example, we consider the shape and time variability of boundary conditions, as well as the randomness of external forces acting on the system. From a practical point of view, one has to estimate statistics of the flow, and a failure probability convergence criterion must be satisfied by the statistical estimator of interest to assess reliability. We use hierarchical Monte Carlo methods as uncertainty quantification strategy to solve stochastic systems. Such algorithms present three levels of parallelism: over levels, over realizations per level, and on the solution of each realization. We propose an improvement by adding a new level of parallelism, between batches, where each batch has its independent hierarchy. These new methods are called asynchronous hierarchical Monte Carlo, and we demonstrate that such techniques take full advantage of concurrency capabilities of modern high performance computing environments, while preserving the same reliability of state of the art methods. Moreover, we focus on reducing the wall clock time required to compute statistical estimators of chaotic incompressible flows. Our approach consists in replacing a single long-term simulation with an ensemble of multiple independent realizations, which are run in parallel with different initial conditions. The error analysis of the statistical estimator leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time to minimize the initialization bias, accompanied by strategies to reduce the simulation cost. Finally, we propose an integration of Monte Carlo and ensemble averaging methods for reducing the wall clock time required for computing statistical estimators of time-dependent stochastic turbulent flows. A single long-term Monte Carlo realization is replaced by an ensemble of multiple independent realizations, each characterized by the same random event and different initial conditions. We consider different systems, relevant in the computational fluid dynamics engineering field, as realistic wind flowing around high-rise buildings or compressible potential flow problems. By solving such numerical examples, we demonstrate the accuracy, efficiency, and effectiveness of our proposals.
Los desarrollos relacionados con la computación de alto rendimiento de las últimas décadas permiten resolver problemas científicos actuales, utilizando métodos computacionales sofisticados. Sin embargo, es necesario asegurarse de la eficiencia de los métodos computacionales modernos, con el fin de explotar al máximo las capacidades tecnológicas. En esta tesis proponemos diferentes métodos, relacionados con la cuantificación de incertidumbres y el cálculo de alto rendimiento, con el fin de minimizar el tiempo de computación necesario para resolver las simulaciones y garantizar una alta fiabilidad. En concreto, resolvemos sistemas de dinámica de fluidos caracterizados por incertidumbres. En el campo de la dinámica de fluidos computacional existen diferentes tipos de incertidumbres. Nosotros consideramos, por ejemplo, la forma y la evolución en el tiempo de las condiciones de frontera, así como la aleatoriedad de las fuerzas externas que actúan sobre el sistema. Desde un punto de vista práctico, es necesario estimar valores estadísticos del flujo del fluido, cumpliendo los criterios de convergencia para garantizar la fiabilidad del método. Para cuantificar el efecto de las incertidumbres utilizamos métodos de Monte Carlo jerárquicos, también llamados hierarchical Monte Carlo methods. Estas estrategias tienen tres niveles de paralelización: entre los niveles de la jerarquía, entre los eventos de cada nivel y durante la resolución del evento. Proponemos agregar un nuevo nivel de paralelización, entre batches, en el cual cada batch es independiente de los demás y tiene su propia jerarquía, compuesta por niveles y eventos distribuidos en diferentes niveles. Definimos estos nuevos algoritmos como métodos de Monte Carlo asíncronos y jerárquicos, cuyos nombres equivalentes en inglés son asynchronous hierarchical Monte Carlo methods. También nos enfocamos en reducir el tiempo de computación necesario para calcular estimadores estadísticos de flujos de fluidos caóticos e incompresibles. Nuestro método consiste en reemplazar una única simulación de dinámica de fluidos, caracterizada por una ventana de tiempo prolongada, por el promedio de un conjunto de simulaciones independientes, caracterizadas por diferentes condiciones iniciales y una ventana de tiempo menor. Este conjunto de simulaciones se puede ejecutar en paralelo en superordenadores, reduciendo el tiempo de computación. El método de promedio de conjuntos se conoce como ensemble averaging. Analizando las diferentes contribuciones del error del estimador estadístico, identificamos dos términos: el error debido a las condiciones iniciales y el error estadístico. En esta tesis proponemos un método que minimiza el error debido a las condiciones iniciales, y en paralelo sugerimos varias estrategias para reducir el coste computacional de la simulación. Finalmente, proponemos una integración del método de Monte Carlo y del método de ensemble averaging, cuyo objetivo es reducir el tiempo de computación requerido para calcular estimadores estadísticos de problemas de dinámica de fluidos dependientes del tiempo, caóticos y estocásticos. Reemplazamos cada realización de Monte Carlo por un conjunto de realizaciones independientes, cada una caracterizada por el mismo evento aleatorio y diferentes condiciones iniciales. Consideramos y resolvemos diferentes sistemas físicos, todos relevantes en el campo de la dinámica de fluidos computacional, como problemas de flujo del viento alrededor de rascacielos o problemas de flujo potencial. Demostramos la precisión, eficiencia y efectividad de nuestras propuestas resolviendo estos ejemplos numéricos.
Gli sviluppi del calcolo ad alte prestazioni degli ultimi decenni permettono di risolvere problemi scientifici di grande attualità, utilizzando sofisticati metodi computazionali. È però necessario assicurarsi dell’efficienza di questi metodi, in modo da ottimizzare l’uso delle odierne conoscenze tecnologiche. A tal fine, in questa tesi proponiamo diversi metodi, tutti inerenti ai temi di quantificazione di incertezze e calcolo ad alte prestazioni. L’obiettivo è minimizzare il tempo necessario per risolvere le simulazioni e garantire alta affidabilità. Nello specifico, utilizziamo queste strategie per risolvere sistemi fluidodinamici caratterizzati da incertezze in macchine ad alte prestazioni. Nel campo della fluidodinamica computazionale esistono diverse tipologie di incertezze. In questo lavoro consideriamo, ad esempio, il valore e l’evoluzione temporale delle condizioni di contorno, così come l’aleatorietà delle forze esterne che agiscono sul sistema fisico. Dal punto di vista pratico, è necessario calcolare una stima delle variabili statistiche del flusso del fluido, soddisfacendo criteri di convergenza, i quali garantiscono l’accuratezza del metodo. Per quantificare l’effetto delle incertezze sul sistema utilizziamo metodi gerarchici di Monte Carlo, detti anche hierarchical Monte Carlo methods. Queste strategie presentano tre livelli di parallelizzazione: tra i livelli della gerarchia, tra gli eventi di ciascun livello e durante la risoluzione del singolo evento. Proponiamo di aggiungere un nuovo livello di parallelizzazione, tra gruppi (batches), in cui ogni batch sia indipendente dagli altri ed abbia una propria gerarchia, composta da livelli e da eventi distribuiti su diversi livelli. Definiamo questi nuovi algoritmi come metodi asincroni e gerarchici di Monte Carlo, il cui corrispondente in inglese è asynchronous hierarchical Monte Carlo methods. Ci focalizziamo inoltre sulla riduzione del tempo di calcolo necessario per stimare variabili statistiche di flussi caotici ed incomprimibili. Il nostro metodo consiste nel sostituire un’unica simulazione fluidodinamica, caratterizzata da un lungo arco temporale, con il valore medio di un insieme di simulazioni indipendenti, caratterizzate da diverse condizioni iniziali ed un arco temporale minore. Questo insieme 10 di simulazioni può essere eseguito in parallelo in un supercomputer, riducendo il tempo di calcolo. Questo metodo è noto come media di un insieme o, in inglese, ensemble averaging. Calcolando la stima di variabili statistiche, commettiamo due errori: l’errore dovuto alle condizioni iniziali e l’errore statistico. In questa tesi proponiamo un metodo per minimizzare l’errore dovuto alle condizioni iniziali, ed in parallelo suggeriamo diverse strategie per ridurre il costo computazionale della simulazione. Infine, proponiamo un’integrazione del metodo di Monte Carlo e del metodo di ensemble averaging, il cui obiettivo è ridurre il tempo di calcolo necessario per stimare variabili statistiche di problemi di fluidodinamica dipendenti dal tempo, caotici e stocastici. Ogni realizzazione di Monte Carlo è sostituita da un insieme di simulazioni indipendenti, ciascuna caratterizzata dallo stesso evento casuale, da differenti condizioni iniziali e da un arco temporale minore. Consideriamo e risolviamo differenti sistemi fisici, tutti rilevanti nel campo della fluidodinamica computazionale, come per esempio problemi di flusso del vento attorno a grattacieli, o sistemi di flusso potenziale. Dimostriamo l’accuratezza, l’efficienza e l’efficacia delle nostre proposte, risolvendo questi esempi numerici.
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Szekely, Tamas. "Stochastic modelling and simulation in cell biology." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f9b8dbe6-d96d-414c-ac06-909cff639f8c.

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Modelling and simulation are essential to modern research in cell biology. This thesis follows a journey starting from the construction of new stochastic methods for discrete biochemical systems to using them to simulate a population of interacting haematopoietic stem cell lineages. The first part of this thesis is on discrete stochastic methods. We develop two new methods, the stochastic extrapolation framework and the Stochastic Bulirsch-Stoer methods. These are based on the Richardson extrapolation technique, which is widely used in ordinary differential equation solvers. We believed that it would also be useful in the stochastic regime, and this turned out to be true. The stochastic extrapolation framework is a scheme that admits any stochastic method with a fixed stepsize and known global error expansion. It can improve the weak order of the moments of these methods by cancelling the leading terms in the global error. Using numerical simulations, we demonstrate that this is the case up to second order, and postulate that this also follows for higher order. Our simulations show that extrapolation can greatly improve the accuracy of a numerical method. The Stochastic Bulirsch-Stoer method is another highly accurate stochastic solver. Furthermore, using numerical simulations we find that it is able to better retain its high accuracy for larger timesteps than competing methods, meaning it remains accurate even when simulation time is speeded up. This is a useful property for simulating the complex systems that researchers are often interested in today. The second part of the thesis is concerned with modelling a haematopoietic stem cell system, which consists of many interacting niche lineages. We use a vectorised tau-leap method to examine the differences between a deterministic and a stochastic model of the system, and investigate how coupling niche lineages affects the dynamics of the system at the homeostatic state as well as after a perturbation. We find that larger coupling allows the system to find the optimal steady state blood cell levels. In addition, when the perturbation is applied randomly to the entire system, larger coupling also results in smaller post-perturbation cell fluctuations compared to non-coupled cells. In brief, this thesis contains four main sets of contributions: two new high-accuracy discrete stochastic methods that have been numerically tested, an improvement that can be used with any leaping method that introduces vectorisation as well as how to use a common stepsize adapting scheme, and an investigation of the effects of coupling lineages in a heterogeneous population of haematopoietic stem cell niche lineages.
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Books on the topic "Computational stochastic dynamics"

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Papadrakakis, Manolis, George Stefanou, and Vissarion Papadopoulos, eds. Computational Methods in Stochastic Dynamics. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9987-7.

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Winkelmann, Stefanie, and Christof Schütte. Stochastic Dynamics in Computational Biology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62387-6.

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Papadrakakis, Manolis, George Stefanou, and Vissarion Papadopoulos, eds. Computational Methods in Stochastic Dynamics. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7.

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Papadrakakis, Manolis. Computational Methods in Stochastic Dynamics: Volume 2. Dordrecht: Springer Netherlands, 2013.

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Öttinger, Hans Christian. Stochastic processes in polymeric fluids: Tools and examples for developing simulation algorithms. Berlin: Springer, 1996.

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Schinazi, Rinaldo B. Classical and Spatial Stochastic Processes. Boston, MA: Birkhäuser Boston, 1999.

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Nonlinear and Stochastic Beam Dynamics in Accelorators. (1993 Desy, Lüneburg). Nonlinear and stochastic beam dynamics in accelerators: A challenge to theoretical and computational physics, Lüneburg, September 29-October 3, 1997. Hamburg: Deutsches Elektronen-Synchrotron, 1998.

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Krzysztof, Szajowski, and SpringerLink (Online service), eds. Advances in Dynamic Games: Theory, Applications, and Numerical Methods for Differential and Stochastic Games. Boston: Springer Science+Business Media, LLC, 2011.

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service), SpringerLink (Online, ed. Modeling Multi-Level Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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Book chapters on the topic "Computational stochastic dynamics"

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Bucher, C. G., H. J. Pradlwarter, and G. I. Schuëller. "Computational Stochastic Structural Analysis (COSSAN)." In Structural Dynamics, 301–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-88298-2_13.

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Winkelmann, Stefanie, and Christof Schütte. "Well-Mixed Stochastic Reaction Kinetics." In Stochastic Dynamics in Computational Biology, 1–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62387-6_1.

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Matthies, Hermann G., and Elmar Zander. "Sparse Representations in Stochastic Mechanics." In Computational Methods in Stochastic Dynamics, 247–65. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9987-7_13.

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Xu, X. Frank, and George Stefanou. "Computational Stochastic Dynamics Based on Orthogonal Expansion of Random Excitations." In Computational Methods in Stochastic Dynamics, 55–67. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_4.

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Batou, Anas, and Christian Soize. "Random Dynamical Response of a Multibody System with Uncertain Rigid Bodies." In Computational Methods in Stochastic Dynamics, 1–14. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_1.

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Jensen, Hector A., Marcos A. Valdebenito, and Juan G. Sepulveda. "Optimal Design of Base-Isolated Systems Under Stochastic Earthquake Excitation." In Computational Methods in Stochastic Dynamics, 161–78. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_10.

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Moutsopoulou, Amalia, Georgios E. Stavroulakis, and Anastasios Pouliezos. "Systematic Formulation of Model Uncertainties and Robust Control in Smart Structures Using H ∞ and μ-Analysis." In Computational Methods in Stochastic Dynamics, 179–202. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_11.

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Saad, George A., and Roger G. Ghanem. "Robust Structural Health Monitoring Using a Polynomial Chaos Based Sequential Data Assimilation Technique." In Computational Methods in Stochastic Dynamics, 203–13. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_12.

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Goller, B., M. Broggi, A. Calvi, and G. I. Schuëller. "Efficient Model Updating of the GOCE Satellite Based on Experimental Modal Data." In Computational Methods in Stochastic Dynamics, 215–35. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_13.

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Rosić, Bojana V., and Hermann G. Matthies. "Identification of Properties of Stochastic Elastoplastic Systems." In Computational Methods in Stochastic Dynamics, 237–53. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_14.

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Conference papers on the topic "Computational stochastic dynamics"

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P., Spanos, Pirrotta A., Marino F., and Robledo Ricardo L. A. "Stochastic Analysis of Motorcycle Dynamics." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p056.

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Aly, Sherif, Madara Ogot, Richard Pelz, Frank Marconi, and Mike Siclari. "Stochastic optimization applied to CFD shape design." In 12th Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-1647.

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Belenky, Vadim, Kenneth Weems, Christopher Bassler, Martin Dipper, Bradley Campbell, and Kostas Spyrou. "Approaches to Rare Events in Stochastic Dynamics of Ships." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p009.

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Pettersson, Per, Gianluca Iaccarino, and Jan Nordström. "Boundary Procedures for the Time-Dependent Stochastic Burgers' Equation." In 19th AIAA Computational Fluid Dynamics. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-3550.

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Duraisamy, Karthikeyan, Juan Alonso, and Praveen Chandrashekar. "Goal Oriented Uncertainty Propagation using Stochastic Adjoints." In 20th AIAA Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-3412.

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To, C. W. S., Jane W. Z. Lu, Andrew Y. T. Leung, Vai Pan Iu, and Kai Meng Mok. "Symplectic Algorithms in Computational Stochastic Nonlinear Structural Dynamics." In PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF COMPUTATIONAL METHODS IN ENGINEERING AND SCIENCE. AIP, 2010. http://dx.doi.org/10.1063/1.3452191.

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Nair, Prasanth, and Andy Keane. "New developments in computational stochastic mechanics. II - Applications." In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-1441.

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Nair, Prasanth, and Andy Keane. "New developments in computational stochastic mechanics. I - Theory." In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-1827.

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Witteveen, Jeroen, and Hester Bijl. "Uncertainty Quantification in Fluid-Structure Interaction Simulations Using a Simplex Elements Stochastic Collocation Approach." In 19th AIAA Computational Fluid Dynamics. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-3671.

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R. V., Field Jr, and Grigoriu M. "A Poisson Random Field Model for the Dynamics of Laminar-to-Turbulent Transition on a Flight Vehicle." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p027.

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Reports on the topic "Computational stochastic dynamics"

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Chen, Xin, Yanfeng Ouyang, Ebrahim Arian, Haolin Yang, and Xingyu Ba. Modeling and Testing Autonomous and Shared Multimodal Mobility Services for Low-Density Rural Areas. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-013.

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Recent developments in transformative technologies hold the promise to provide holistic solutions for affordable transportation services to rural areas and thus greatly alleviate existing social inequality through efficient planning and management of complex transportation systems and systemwide interactions among multiple modes. To realize the promise, many challenging research questions need to be addressed, which often leads to computationally intractable, large-scale, dynamic/stochastic, discrete optimization models. This project proposes to address some of the challenges by building a series of holistic and tractable models on the design of mobility services, capacity planning, dynamic matching, and routing, as well as pricing. The proposed project is expected to create a new series of planning and management models that can support strategical and operational decisions for large-scale autonomous and shared mobility systems in rural areas. The planned case study and simulation for the Village of Rantoul, Illinois, will lay the foundation for future field implementation.
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