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Hellander, Stefan. "Stochastic Simulation of Reaction-Diffusion Processes." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198522.
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Numerical simulation methods have become an important tool in the study of chemical reaction networks in living cells. Many systems can, with high accuracy, be modeled by deterministic ordinary differential equations, but other systems require a more detailed level of modeling. Stochastic models at either the mesoscopic level or the microscopic level can be used for cases when molecules are present in low copy numbers. In this thesis we develop efficient and flexible algorithms for simulating systems at the microscopic level. We propose an improvement to the Green's function reaction dynamics algorithm, an efficient microscale method. Furthermore, we describe how to simulate interactions with complex internal structures such as membranes and dynamic fibers. The mesoscopic level is related to the microscopic level through the reaction rates at the respective scale. We derive that relation in both two dimensions and three dimensions and show that the mesoscopic model breaks down if the discretization of space becomes too fine. For a simple model problem we can show exactly when this breakdown occurs. We show how to couple the microscopic scale with the mesoscopic scale in a hybrid method. Using the fact that some systems only display microscale behaviour in parts of the system, we can gain computational time by restricting the fine-grained microscopic simulations to only a part of the system. Finally, we have developed a mesoscopic method that couples simulations in three dimensions with simulations on general embedded lines. The accuracy of the method has been verified by comparing the results with purely microscopic simulations as well as with theoretical predictions.eSSENCE
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Drawert, Brian J. "Spatial Stochastic Simulation of Biochemical Systems." Thesis, University of California, Santa Barbara, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3559784.
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Recent advances in biology have shown that proteins and genes often interact probabilistically. The resulting effects that arise from these stochastic dynamics differ significantly than traditional deterministic formulations, and have biologically significant ramifications. This has led to the development of computational models of the discrete stochastic biochemical pathways found in living organisms. These include spatial stochastic models, where the physical extent of the domain plays an important role; analogous to traditional partial differential equations.
Simulation of spatial stochastic models is a computationally intensive task. We have developed a new algorithm, the Diffusive Finite State Projection (DFSP) method for the efficient and accurate simulation of stochastic spatially inhomogeneous biochemical systems. DFSP makes use of a novel formulation of Finite State Projection (FSP) to simulate diffusion, while reactions are handled by the Stochastic Simulation Algorithm (SSA). Further, we adapt DFSP to three dimensional, unstructured, tetrahedral meshes in inclusion in the mature and widely usable systems biology modeling software URDME, enabling simulation of the complex geometries found in biological systems. Additionally, we extend DFSP with adaptive error control and a highly efficient parallel implementation for the graphics processing units (GPU).
In an effort to understand biological processes that exhibit stochastic dynamics, we have developed a spatial stochastic model of cellular polarization. Specifically we investigate the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrates that higher levels of stochastic noise results in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations.
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Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.
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Morton-Firth, Carl Jason. "Stochastic simulation of cell signalling pathways." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625063.
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Cheung, Ricky. "Stochastic based football simulation using data." Thesis, Uppsala universitet, Matematiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359835.
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This thesis is an extension of a football simulator made in a previous project, where we also made different visualizations and simulators based on football data. The goal is to create a football simulator based on a modified Markov chain process, where two teams can be chosen, to simulate entire football matches play-by-play. To validate our model, we compare simulated data with the provided data from Opta. Several adjustments are made to make the simulation as realistic as possible. After conducting a few experiments to compare simulated data with real data before and after adjustments, we conclude that the model may not be adequately accurate to reflect real life matches.
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Du, Manuel. "Stochastic simulation studies for honeybee breeding." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22295.
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Die Arbeit beschreibt ein stochastisches Simulationsprogramm zur Modellierung von Honigbienenpopulationen unter Zuchtbedingungen. Dieses Programm wurde neu implementiert um unterschiedliche Selektionsstrategien zu evaluieren und zu optimieren. In einer ersten Studie wurde untersucht, inwiefern die Vorhersagen, die das Programm trifft, vom verwendeten genetischen Modell abhängt. Hierbei wurde festgestellt, dass das Finite-Locus-Modell dem Infinitesimalmodell in Langzeitstudien vorzuziehen ist. Eine zweite Studie beleuchtete die Bedeutung der sicheren Anpaarung auf Belegstellen für die Honigbienenzucht. Hier zeigten die Simulationen, dass die Zucht mit Anpaarungskontrolle derjenigen mit freier Paarung von Königinnen deutlich überlegen ist. Schließlich wurde in einer finalen Studie der Frage nachgegangen, wie erfolgreiche Zuchtprogramme bei der Honigbiene langfristig nachhaltig zu gestalten sind. Hierbei sind kurzfristiger genetischer Zugewinn und langfristige Inzuchtvermeidung gegeneinander abzuwägen. Durch umfangreiche Simulationen konnten für verschiedene Ausgangspopulationen Empfehlungen für eine optimale Zuchtintensität auf mütterlicher und väterlicher Seite gefunden werden.The present work describes a stochastic simulation program for modelling honeybee populations under breeding conditions. The program was newly implemented to investigate and optimize different selection strategies. A first study evaluated in how far the program's predictions depend on the underlying genetic model. It was found that the finite locus model rather than the infinitesimal model should be used for long-term investigations. A second study shed light into the importance of controlled mating for honeybee breeding. It was found that breeding schemes with controlled mating are far superior to free-mating alternatives. Ultimately, a final study examined how successful breeding strategies can be designed so that they are sustainable in the long term. For this, short-term genetic progress has to be weighed against the avoidance of inbreeding in the long run. By extensive simulations, optimal selection intensities on the maternal and paternal paths could be determined for different sets of population parameters.
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Vasan, Arunchandar. "Timestepped stochastic simulation of 802.11 WLANs." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8533.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.
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We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, allowing the implicit specification of functions within the optimization problem, thereby providing the ability to embed virtually any level of complexity. The result of such versatility has been evident in SOs ready adoption in fields as varied as finance, logistics, healthcare, and telecommunication systems.
While SO has become popular over the years, Robbins and Monros original stochastic approximation algorithm and its numerous modern incarnations have seen only mixed success in solving SO problems. The primary reason for this is stochastic approximations explicit reliance on a sequence of algorithmic parameters to guarantee convergence. The theory for choosing such parameters is now well-established, but most such theory focuses on asymptotic performance. Automatically choosing parameters to ensure good finite-time performance has remained vexingly elusive, as evidenced by continuing efforts six decades after the introduction of stochastic approximation! The other popular paradigm to solve SO is what has been called sample-average approximation. Sample-average approximation, more a philosophy than an algorithm to solve SO, attempts to leverage advances in modern nonlinear programming by first constructing a deterministic approximation of the SO problem using a fixed sample size, and then applying an appropriate nonlinear programming method. Sample-average approximation is reasonable as a solution paradigm but again suffers from finite-time inefficiency because of the simplistic manner in which sample sizes are prescribed. It turns out that in many SO contexts, the effort expended to execute the Monte Carlo oracle is the single most computationally expensive operation. Sample-average approximation essentially ignores this issue since, irrespective of where in the search space an incumbent solution resides, prescriptions for sample sizes within sample-average approximation remain the same. Like stochastic approximation, notwithstanding beautiful asymptotic theory, sample-average approximation suffers from the lack of automatic implementations that guarantee good finite-time performance.
In this dissertation, we ask: can advances in algorithmic nonlinear programming theory be combined with intelligent sampling to create solution paradigms for SO that perform well in finite-time while exhibiting asymptotically optimal convergence rates? We propose and study a general solution paradigm called Sampling Controlled Stochastic Recursion (SCSR). Two simple ideas are central to SCSR: (i) use any recursion, particularly one that you would use (e.g., Newton and quasi- Newton, fixed-point, trust-region, and derivative-free recursions) if the functions involved in the problem were known through a deterministic oracle; and (ii) estimate objects appearing within the recursions (e.g., function derivatives) using Monte Carlo sampling to the extent required. The idea in (i) exploits advances in algorithmic nonlinear programming. The idea in (ii), with the objective of ensuring good finite-time performance and optimal asymptotic rates, minimizes Monte Carlo sampling by attempting to balance the estimated proximity of an incumbent solution with the sampling error stemming from Monte Carlo. This dissertation studies the theoretical and practical underpinnings of SCSR, leading to implementable algorithms to solve SO. We first analyze SCSR in a general context, identifying various sufficient conditions that ensure convergence of SCSRs iterates to a solution. We then analyze the nature of such convergence. For instance, we demonstrate that in SCSRs which guarantee optimal convergence rates, the speed of the underlying (deterministic) recursion and the extent of Monte Carlo sampling are intimately linked, with faster recursions permitting a wider range of Monte Carlo effort. With the objective of translating such asymptotic results into usable algorithms, we formulate a family of SCSRs called Adaptive SCSR (A-SCSR) that adaptively determines how much to sample as a recursion evolves through the search space. A-SCSRs are dynamic algorithms that identify sample sizes to balance estimated squared bias and variance of an incumbent solution. This makes the sample size (at every iteration of A-SCSR) a stopping time, thereby substantially complicating the analysis of the behavior of A-SCSRs iterates. That A-SCSR works well in practice is not surprising" the use of an appropriate recursion and the careful sample size choice ensures this. Remarkably, however, we show that A-SCSRs are convergent to a solution and exhibit asymptotically optimal convergence rates under conditions that are no less general than what has been established for stochastic approximation algorithms.
We end with the application of a certain A-SCSR to a parameter estimation problem arising in the context of brain-computer interfaces (BCI). Specifically, we formulate and reduce the problem of probabilistically deciphering the electroencephalograph (EEG) signals recorded from the brain of a paralyzed patient attempting to perform one of a specified set of tasks. Monte Carlo simulation in this context takes a more general view, as the act of drawing an observation from a large dataset accumulated from the recorded EEG signals. We apply A-SCSR to nine such datasets, showing that in most cases A-SCSR achieves correct prediction rates that are between 5 and 15 percent better than competing algorithms. More importantly, due to the incorporated adaptive sampling strategies, A-SCSR tends to exhibit dramatically better efficiency rates for comparable prediction accuracies.Ph. D.
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Albertyn, Martin. "Generic simulation modelling of stochastic continuous systems." Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.
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Xu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.
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Recent research in experimental and computational biology has revealed the necessity of using stochastic modeling and simulation to investigate the functionality and dynamics of gene networks. However, there is no sophisticated stochastic modeling techniques and efficient stochastic simulation algorithms (SSA) for analyzing and simulating gene networks. Therefore, the objective of this research is to design highly efficient and accurate SSAs, to develop stochastic models for certain real gene networks and to apply stochastic simulation to investigate such gene networks. To achieve this objective, we developed several novel efficient and accurate SSAs. We also proposed two stochastic models for the circadian system of Drosophila and simulated the dynamics of the system. The K-leap method constrains the total number of reactions in one leap to a properly chosen number thereby improving simulation accuracy. Since the exact SSA is a special case of the K-leap method when K=1, the K-leap method can naturally change from the exact SSA to an approximate leap method during simulation if necessary. The hybrid tau/K-leap and the modified K-leap methods are particularly suitable for simulating gene networks where certain reactant molecular species have a small number of molecules. Although the existing tau-leap methods can significantly speed up stochastic simulation of certain gene networks, the mean of the number of firings of each reaction channel is not equal to the true mean. Therefore, all existing tau-leap methods produce biased results, which limit simulation accuracy and speed. Our unbiased tau-leap methods remove the bias in simulation results that exist in all current leap SSAs and therefore significantly improve simulation accuracy without sacrificing speed. In order to efficiently estimate the probability of rare events in gene networks, we applied the importance sampling technique to the next reaction method (NRM) of the SSA and developed a weighted NRM (wNRM). We further developed a systematic method for selecting the values of importance sampling parameters. Applying our parameter selection method to the wSSA and the wNRM, we get an improved wSSA (iwSSA) and an improved wNRM (iwNRM), which can provide substantial improvement over the wSSA in terms of simulation efficiency and accuracy. We also develop a detailed and a reduced stochastic model for circadian rhythm in Drosophila and employ our SSA to simulate circadian oscillations. Our simulations showed that both models could produce sustained oscillations and that the oscillation is robust to noise in the sense that there is very little variability in oscillation period although there are significant random fluctuations in oscillation peeks. Moreover, although average time delays are essential to simulation of oscillation, random changes in time delays within certain range around fixed average time delay cause little variability in the oscillation period. Our simulation results also showed that both models are robust to parameter variations and that oscillation can be entrained by light/dark circles.
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Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.
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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to zero almost surely for any strictly feasible solution under certain conditions. For a feasible solution located on the boundary of feasible and infeasible regions, the sequence converges to zero either with high probability or almost surely. As a result, a DOvS algorithm combined with PFM performs well even when optimal solutions are tight or nearly tight. Second, we design an optimal water quality monitoring network for river systems. The problem is to find the optimal location of a finite number of monitoring devices, minimizing the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic DOvS problem with the objective of minimizing expected detection time and with a stochastic constraint on the detection reliability; and it is solved by a DOvS algorithm combined with PFM. Finally, we improve PFM by combining it with an approximate budget allocation procedure. We revise an existing optimal budget allocation procedure so that it can handle active constraints and satisfy necessary conditions for the convergence of PFM.
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Chaleeraktrakoon, Chavalit. "Stochastic modelling and simulation of streamflow processes." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28704.
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The main objectives of this research are to propose a general stochastic method for determining analytically the distribution of flood volume, and to develop a simulation procedure for generating synthetic multiseason streamflows at different sites simultaneously. The research study is divided into two parts: (a) First, a general stochastic model is proposed to derive analytically the probability distribution function for flood volume. The volume of a flood is defined as the sum of an unbroken sequence of consecutive daily flows above a given truncation level. Analytical expressions were then derived for the exact distribution of flood volume for various cases in which successive flow exceedances can be assumed to be either independent or correlated, and the cumulative flow exceedance can be considered to be independent or dependent of the corresponding flood duration. The proposed stochastic method is more general and more flexible than empirical fitting approach because it can take explicitly into account different stochastic properties inherent in the underlying streamflow process. Results of a numerical application have shown that the proposed model could provide a very good fit between observed and theoretical results. Further, in the estimation of flood volume distribution, it has been found that the effect of the serial correlation property of the flow series is not significant as compared to the impact due to the dependence between flood volume and corresponding duration. Finally, it has been demonstrated that the simplistic assumption of triangular flood hydrograph shape, as usually appeared in previous studies, is not necessary in the estimation of flood volume distribution. (b) Second, a multivariate stochastic simulation approach is proposed for generating synthetic seasonal streamflow series at a single location or at many different locations concurrently. The suggested simulation scheme is based on the combination of the singular value decomposition
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Minoukadeh, Kimiya. "Deterministic and stochastic methods for molecular simulation." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00597694.
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Molecular simulation is an essential tool in understanding complex chemical and biochemical processes as real-life experiments prove increasingly costly or infeasible in practice . This thesis is devoted to methodological aspects of molecular simulation, with a particular focus on computing transition paths and their associated free energy profiles. The first part is dedicated to computational methods for reaction path and transition state searches on a potential energy surface. In Chapter 3 we propose an improvement to a widely-used transition state search method, the Activation Relaxation Technique (ART). We also present a local convergence study of a prototypical algorithm. The second part is dedicated to free energy computations. We focus in particular on an adaptive importance sampling technique, the Adaptive Biasing Force (ABF) method. The first contribution to this field, presented in Chapter 5, consists in showing the applicability to a large molecular system of a new parallel implementation, named multiple-walker ABF (MW-ABF). Numerical experiments demonstrated the robustness of MW-ABF against artefacts arising due to poorly chosen or oversimplified reaction coordinates. These numerical findings inspired a new study of the longtime convergence of the ABF method, as presented in Chapter 6. By studying a slightly modified model, we back our numerical results by showing a faster theoretical rate of convergence of ABF than was previously shown
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Wang, Eric Yiqing. "Comparison Between Deterministic and Stochastic Biological Simulation." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230732.
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McCloughan, Patrick. "A stochastic simulation model of industrial concentration." Thesis, University of East Anglia, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333558.
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Hardy, Mary Rosalyn. "Stochastic simulation in life office solvency assessment." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1398.
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Liu, Kuo-Ching. "Stochastic simulation-based finite capacity scheduling systems /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487946776022111.
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Geurts, Kevin Richard. "Stochastic simulation of non-Newtonian flow fields /." Thesis, Connect to this title online; UW restricted, 1995. http://hdl.handle.net/1773/9821.
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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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Pahle, Jürgen. "Stochastic simulation and analysis of biochemical networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2008. http://dx.doi.org/10.18452/15786.
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Stochastische Effekte können einen großen Einfluss auf die Funktionsweise von biochemischen Netzwerken haben. Vor allem Signalwege, z.B. Calciumsignaltransduktion, sind anfällig gegenüber zufälligen Schwankungen. Daher stellt sich die wichtige Frage, wie dadurch der Informationstransfer in diesen Systemen beeinträchtigt wird. Zunächst werden eine Reihe von stochastischen Simulationsmethoden diskutiert und systematisch klassifiziert. Dies dient als methodische Grundlage der ganzen Dissertation. Der Schwerpunkt liegt hier auf approximativen und hybriden Ansätzen, einschließlich der Hybridmethode des Softwaresystems Copasi, deren Implementierung Teil dieser Arbeit war. Die Dynamik biochemischer Systeme zeigt in den meisten Fällen einen Übergang von stochastischem zu deterministischem Verhalten mit steigender Partikelzahl. Dieser Übergang wird für Calciumsignaltransduktion und andere Systeme untersucht. Es zeigt sich, dass das Auftreten stochastischer Effekte stark von der Sensitivität des Systems abhängt. Ein Maß dafür ist die Divergenz. Systeme mit hoher Divergenz zeigen noch mit hohen Teilchenzahlen stochastische Effekte und umgekehrt. Schließlich wird der Einfluss von zufälligen Fluktuationen auf die Leistungsfähigkeit von Signalpfaden erforscht. Dazu werden simulierte sowie experimentell gemessene Calcium-Zeitreihen stochastisch an die Aktivierung eines Zielenzyms gekoppelt. Das Schätzen des informationstheoretischen Maßes Transferentropie unter unterschiedlichen zellulären Bedingungen dient zur Abschätzung des Informationstransfers. Dieser nimmt mit steigender Partikelzahl zu, ist jedoch sehr abhängig von der momentanen Dynamik (z.B. spikende, burstende oder irreguläre Oszillationen). Die hier entwickelten Methoden, wie der Gebrauch der Divergenz als Indikator für den stoch./det. Übergang oder die stochastische Kopplung und informationstheoretische Analyse mittels Transferentropie, sind wertvolle Werkzeuge für die Analyse von biochemischen Systemen.Stochastic effects in biochemical networks can affect the functioning of these systems significantly. Signaling pathways, such as calcium signal transduction, are particularly prone to random fluctuations. Thus, an important question is how this influences the information transfer in these pathways. First, a comprehensive overview and systematic classification of stochastic simulation methods is given as methodical basis for the thesis. Here, the focus is on approximate and hybrid approaches. Also, the hybrid solver in the software system Copasi is described whose implementation was part of this PhD work. Then, in most cases, the dynamic behavior of biochemical systems shows a transition from stochastic to deterministic behavior with increasing particle numbers. This transition is studied in calcium signaling as well as other test systems. It turns out that the onset of stochastic effects is very dependent on the sensitivity of the specific system quantified by its divergence. Systems with high divergence show stochastic effects even with high particle numbers and vice versa. Finally, the influence of noise on the performance of signaling pathways is investigated. Simulated and experimentally measured calcium time series are stochastically coupled to an intracellular target enzyme activation process. Then, the information transfer under different cellular conditions is estimated with the information-theoretic quantity transfer entropy. The amount of information that can be transferred increases with rising particle numbers. However, this increase is very dependent on the current dynamical mode of the system, such as spiking, bursting or irregular oscillations. The methods developed in this thesis, such as the use of the divergence as an indicator for the transition from stochastic to deterministic behavior or the stochastic coupling and information-theoretic analysis using transfer entropy, are valuable tools for the analysis of biochemical systems.
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Stocks, Nigel Geoffrey. "Experiments in stochastic nonlinear dynamics." Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315224.
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Olsén, Jörgen. "Stochastic Modeling and Simulation of the TCP protocol." Doctoral thesis, Uppsala University, Mathematical Statistics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.
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The success of the current Internet relies to a large extent on a cooperation between the users and the network. The network signals its current state to the users by marking or dropping packets. The users then strive to maximize the sending rate without causing network congestion. To achieve this, the users implement a flow-control algorithm that controls the rate at which data packets are sent into the Internet. More specifically, the Transmission Control Protocol (TCP) is used by the users to adjust the sending rate in response to changing network conditions. TCP uses the observation of packet loss events and estimates of the round trip time (RTT) to adjust its sending rate.
In this thesis we investigate and propose stochastic models for TCP. The models are used to estimate network performance like throughput, link utilization, and packet loss rate. The first part of the thesis introduces the TCP protocol and contains an extensive TCP modeling survey that summarizes the most important TCP modeling work. Reviewed models are categorized as renewal theory models, fixed-point methods, fluid models, processor sharing models or control theoretic models. The merits of respective category is discussed and guidelines for which framework to use for future TCP modeling is given.
The second part of the thesis contains six papers on TCP modeling. Within the renewal theory framework we propose single source TCP-Tahoe and TCP-NewReno models. We investigate the performance of these protocols in both a DropTail and a RED queuing environment. The aspects of TCP performance that are inherently depending on the actual implementation of the flow-control algorithm are singled out from what depends on the queuing environment.
Using the fixed-point framework, we propose models that estimate packet loss rate and link utilization for a network with multiple TCP-Vegas, TCP-SACK and TCP-Reno on/off sources. The TCP-Vegas model is novel and is the first model capable of estimating the network's operating point for TCP-Vegas sources sending on/off traffic. All TCP and network models in the contributed research papers are validated via simulations with the network simulator ns-2.
This thesis serves both as an introduction to TCP and as an extensive orientation about state of the art stochastic TCP models.
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Liss, Anders. "Optimizing stochastic simulation of a neuron with parallelization." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324444.
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In order to optimize the solving of stochastic simulations of neuron channels, an attempt to parallelize the solver has been made. The result of the implementation was unsuccessful. However, the implementation is not impossible and is still a field of research with big potential for improving performance of stochastic simulations.
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Olsén, Jörgen. "Stochastic modeling and simulation of the TCP protocol /." Uppsala : Matematiska institutionen, Univ. [distributör], 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.
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Pérez, Godofredo. "Stochastic conditional simulation for description of reservoir properties /." Access abstract and link to full text, 1991. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9203796.
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Siu, Daniel. "Stochastic Hybrid Dynamic Systems: Modeling, Estimation and Simulation." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4405.
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Stochastic hybrid dynamic systems that incorporate both continuous and discrete dynamics have been an area of great interest over the recent years. In view of applications, stochastic hybrid dynamic systems have been employed to diverse fields of studies, such as communication networks, air traffic management, and insurance risk models. The aim of the present study is to investigate properties of some classes of stochastic hybrid dynamic systems.
The class of stochastic hybrid dynamic systems investigated has random jumps driven by a non-homogeneous Poisson process and deterministic jumps triggered by hitting the boundary. Its real-valued continuous dynamic between jumps is described by stochastic differential equations of the It\^o-Doob type. Existing results of piecewise deterministic models are extended to obtain the infinitesimal generator of the stochastic hybrid dynamic systems through a martingale approach. Based on results of the infinitesimal generator, some stochastic stability results are derived. The infinitesimal generator and stochastic stability results can be used to compute the higher moments of the solution process and find a bound of the solution.
Next, the study focuses on a class of multidimensional stochastic hybrid dynamic systems. The continuous dynamic of the systems under investigation is described by a linear non-homogeneous systems of It\^o-Doob type of stochastic differential equations with switching coefficients. The switching takes place at random jump times which are governed by a non-homogeneous Poisson process. Closed form solutions of the stochastic hybrid dynamic systems are obtained. Two important special cases for the above systems are the geometric Brownian motion process with jumps and the Ornstein-Uhlenbeck process with jumps. Based on the closed form solutions, the probability distributions of the solution processes for these two special cases are derived. The derivation employs the use of the modal matrix and transformations.
In addition, the parameter estimation problem for the one-dimensional cases of the geometric Brownian motion and Ornstein-Uhlenbeck processes with jumps are investigated. Through some existing and modified methods, the estimation procedure is presented by first estimating the parameters of the discrete dynamic and subsequently examining the continuous dynamic piecewisely.
Finally, some simulated stochastic hybrid dynamic processes are presented to illustrate the aforementioned parameter-estimation methods. One simulated insurance example is given to demonstrate the use of the estimation and simulation techniques to obtain some desired quantities.
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Du, Manuel [Verfasser]. "Stochastic simulation studies for honeybee breeding / Manuel Du." Berlin : Humboldt-Universität zu Berlin, 2021. http://d-nb.info/1226153194/34.
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Brand, Samuel P. C. "Spatial and stochastic epidemics : theory, simulation and control." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56738/.
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It is now widely acknowledged that spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In this work I investigate an ensemble of techniques for understanding the stochastic dynamics of spatial and discrete epidemic processes, with especial consideration given to SIR disease dynamics for the Levins-type metapopulation. I present a toolbox of techniques for the modeller of spatial epidemics. The highlight results are a novel form of moment closure derived directly from a stochastic differential representation of the epidemic, a stochastic simulation algorithm that asymptotically in system size greatly out-performs existing simulation methods for the spatial epidemic and finally a method for tackling optimal vaccination scheduling problems for controlling the spread of an invasive pathogen.
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L'Ecuyer, Pierre, and Josef Leydold. "rstream: Streams of Random Numbers for Stochastic Simulation." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1544/1/document.pdf.
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The package rstream provides a unified interface to streams of random numbers for the R statistical computing language. Features are: * independent streams of random numbers * substreams * easy handling of streams (initialize, reset) * antithetic random variates The paper describes this packages and demonstrates an simple example the usefulness of this approach.Series: Preprint Series / Department of Applied Statistics and Data Processing
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Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.
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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise.
This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system.
As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data.Doctor of Philosophy
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.
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Mesogitis, Tassos. "Stochastic simulation of the cure of advanced composites." Thesis, Cranfield University, 2015. http://dspace.lib.cranfield.ac.uk/handle/1826/9216.
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This study focuses on the development of a stochastic simulation methodology to study the effects of cure kinetics uncertainty, in plane fibre misalignment and boundary conditions uncertainty on the cure process of composite materials. Differential Scanning Calorimetry was used to characterise cure kinetics variability of a commercial epoxy resin used in aerospace applications. It was found that cure kinetics uncertainty is associated with variations in the initial degree of cure, activation energy and reaction order. Image analysis was employed to characterise in plane fibre misalignment in a carbon fibre ±45º non-crimp fabric. The experimental results showed that variability in tow orientation was significant with a standard deviation of about 1.2º. A set of experiments using an infusion set-up was carried out to quantify boundary conditions uncertainty related to tool temperature, ambient temperature and surface heat transfer coefficient using thermocouples (tool/ambient temperature) and heat flux sensors (surface heat transfer coefficient). It was concluded that boundary conditions uncertainty can show considerable short term and long term variability. Conventional Monte Carlo and Probabilistic Collocation Method were integrated with a thermo-mechanical cure simulation model in order to investigate the effect of cure kinetics, fibre misalignment and boundary conditions variability on process outcome. The cure model was developed and implemented using a finite element model incorporating appropriate material sub-models of cure kinetics, specific heat capacity, thermal conductivity, moduli, thermal expansion and cure shrinkage. The effect of cure kinetics uncertainty on the temperature overshoot of a thick carbon fibre epoxy flat panel was investigated using the two stochastic simulation schemes. The stochastic simulation results showed that variability in cure kinetics can introduce a significant scatter in temperature overshoot, presenting a coefficient of variation of about 30%. Furthermore, it was shown that the collocation method can offer an efficient solution with significantly lower computational cost compared to Monte Carlo at comparable accuracy. Stochastic simulation of the cure of an angle shaped carbon fibre-epoxy component within the Monte Carlo scheme showed that fibre misalignment can cause considerable variability in the process outcome. The coefficient of variation of maximum residual stress can reach up to approximately 2% (standard deviation of 1 MPa) whilst qualitative and quantitative variations in final distortion of the cured part occur with the standard deviation in twist and corner angle reaching values of 0.4 º and 0.05º respectively. Simulation of the cure of a thin carbon fibre-epoxy panel within the Monte Carlo scheme indicated that surface heat transfer and tool temperature variability dominate variability in cure time, resulting in a coefficient of variation of about 22%. In addition to Monte Carlo, the effect of surface heat transfer coefficient and tool temperature variations on cure time was addressed using the collocation method. It was found that probabilistic collocation is capable of capturing variability propagation with good accuracy while offering tremendous benefits in terms of computational costs.
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Chapman, Jacob. "Multi-agent stochastic simulation of occupants in buildings." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/39868/.
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One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage. One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS). No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. A methodology for including rule based models is built into No-MASS, this allows for testing what-if scenarios with building performance simulation and provides a pragmatic basis for the modelling of the behaviours for which there is insufficient data to develop stochastic models. A Belief-Desire-Intention model is used to develop a set of goals and plans that an agent must follow to influence the environment based on their beliefs about current environmental conditions. Recommendations for the future development of stochastic models are presented based on the sensitivity analysis of the plans. A social interactions framework is developed within No-MASS to resolve conflicts between competing agents. This framework resolves situations where each agent may have different desires, for example one may wish to have a window open and another closed based on the outputs of the stochastic models. A votes casting system determines the agent choice, the most votes becomes the action acted on. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules. Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.
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Xu, Lina. "Simulation methods for stochastic differential equations in finance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134388/1/Lina_Xu_Thesis.pdf.
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This thesis resolves a number of econometric problems relating to the use of stochastic differential equations based on computer-intensive simulation methods. Stochastic differential equations play an important role in modern finance. They have been used to model the trajectories of key variables such as short-term interest rates and the volatility of financial assets. The central theme of the thesis is the use of Hermite polynomials to approximate the transitional probability distribution functions of stochastic differential equations. Based on these approximations, a new method is proposed for simulating solutions to these equations and new testing procedures are developed to examine the fit of the equations to observed data.
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Fang, Fang. "A simulation study for Bayesian hierarchical model selection methods." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/fangf/fangfang.pdf.
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Kernstine, Kemp H. "Design space exploration of stochastic system-of-systems simulations using adaptive sequential experiments." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44799.
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The complexities of our surrounding environments are becoming increasingly diverse, more integrated, and continuously more difficult to predict and characterize. These modeling complexities are ever more prevalent in System-of-Systems (SoS) simulations where computational times can surpass real-time and are often dictated by stochastic processes and non-continuous emergent behaviors. As the number of connections continue to increase in modeling environments and the number of external noise variables continue to multiply, these SoS simulations can no longer be explored with traditional means without significantly wasting computational resources.
This research develops and tests an adaptive sequential design of experiments to reduce the computational expense of exploring these complex design spaces. Prior to developing the algorithm, the defining statistical attributes of these spaces are researched and identified. Following this identification, various techniques capable of capturing these features are compared and an algorithm is synthesized. The final algorithm will be shown to improve the exploration of stochastic simulations over existing methods by increasing the global accuracy and computational speed, while reducing the number of simulations required to learn these spaces.
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Sato, Hiroyuki. "Stochastic and simulation models of maritime intercept operations capabilities." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FSato.pdf.
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Thesis (M.S. in Operations Research)--Naval Postgraduate School, December 2005.Thesis Advisor(s): Patricia A. Jacobs, Donald P. Gaver. Includes bibliographical references (p.117-119). Also available online.
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Posadas, Sergio. "Stochastic simulation of a Commander's decision cycle (SSIM CODE)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA392113.
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Thesis (M.S. in Operations Research) Naval Postgraduate School, June 2001.Thesis advisor(s): Paulo, Eugene P. ; Olson, Allen S. "June 2001." Includes bibliographical references (p. 111-115). Also available in print.
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Kochut, Andrzej. "Timestep stochastic simulation of computer networks using diffusion approximation." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2691.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Chalmers, Graeme D. "Implicit numerical simulation of stochastic differential equations with jumps." Thesis, University of Strathclyde, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501657.
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Implicit numerical methods such as the stochastic theta-method offer a practical way to approximate solutions of stochastic differential equations. The method involves a parameter, θ, which is freely chosen. In this thesis, we investigate strong convergence and linear stability, both mean-square and asymptotic, arising from the implementation of the theta-method when applied to ordinary stochastic differential equations incoroporating jumps. Such models are used in several disciplines; in particular, we note their use as models for various financial quantities such as asset prices, interest rates and volatility.
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Löhndorf, Nils, and Stefan Minner. "Simulation Optimization for the Stochastic Economic Lot Scheduling Problem." Taylor and Francis, 2013. http://dx.doi.org/10.1080/0740817X.2012.662310.
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We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to
prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical
study, we compare approximate dynamic programming with a global search for parameters of simple control
policies. We propose two value function approximation schemes based on linear combinations of piecewise-
constant functions as well as control policies that can be described by a small set of parameters. While
approximate value iteration worked well for small problems with three products, it was clearly outperformed
by the global policy search as soon as problem size increased. The most reliable choice in our study was a
globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on
optimal average cost revealed that the cost effect of product diversity was negligible. (authors' abstract)
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Mosbach, Sebastian. "Explicit stochastic and deterministic simulation methods for combustion chemistry." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614282.
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Sidelel, Mihret Getye. "Simulation-Based Stochastic Blockage Model for Millimeter-wave Communication." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276841.
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Recently, the growing demand for high data rates in wireless communication, together with the scarcity of spectrum in existing microwave bands, has motivated the use of millimeter-wave (mmWave) bands for 5G and future wireless communications. Even though mmWaves are potential candidates for fulfilling this rising demand, they come along with their own drawbacks that need to be addressed. Sensitivity to blockages is one of these drawbacks. It is a major channel impairment that is of concern in the design of mmWaves communication systems. As such, different industrial and academic research activities have been performed and are in progress for modeling and characterizing blockages in mmWave communication systems. However, most of the proposed blockage models failed to capture the temporal correlation of the blockages and the dynamics of the channel’s environment.In order to address this issue, this thesis work aims to develop a simple Stochastic Blockage Model for mmWave communication channels. The model uses two-states (ON and OFF states) to represent Line of Sight (LoS) and Non-Line of Sight (NLoS) conditions, respectively. Using simulation-based analysis, the behavior and probability of the LoS and the NLoS situations of a communication link over time have been analyzed. It is demonstrated that the proposed blockage model can capture the behaviors of the probability of a link being blocked or not in a dynamic environment. It was also found to be adequate to model and characterize the effects of blockage in mmWave communication systems. The accuracy of the model was evaluated to be satisfactory by validating the results against a benchmark which was derived from actual data. It is possible to characterize mmWave communication on a system-level by using this model. Thus, this work provides researchers with a simple simulation-based blockage model to help facilitate the study and design of mmWave communication systems.Den växande efterfrågan på höga nedladdningshastigheter i trådlösa kommunikationssystem, i kombination med en brist på tillgängliga frekvensband i nuvarande mikrovågsband, har medfört att millimetervågsbandet ses som ett attraktivt alternativ för 5G och andra framtida trådlösa kommunikationssystem. Även om det är troligt att millimetervågsbandet kommer att vara en del av lösningen för att uppfylla efterfrågeökningen har det en del negativa egenskaper som måste beaktas och tas hänsyn till. Känslighet för blockering är en sådan typisk nackdel som avsevärt försämrar radiokanalen i ett millimetervågssystem. Både från näringslivets sida och inom den akademiska världen har det forskats inom detta område. Den har bestått av karakterisering och framtagning av modeller som beskriver kommunikationssystem på dessa frekvensband med avseende på blockering. Emellertid har de flesta föreslagna modellerna har misslyckats fånga upp de temporala korrelation av de blokeringar och de dynamiken av kanalens miljö.Den här avhandlingen har som mål att utveckla en enkel stokastisk blockeringsmodell för millimetervågsbandet för att ta itu med just dessa frågor. Modellen använder två tillstånd (ON och OFF tillstånd) för att representera Line of Sight (LoS) respektive Non-Line of Sight (NLoS) utbredning. Simuleringar av kanalen har gjorts för att analysera de tidsmässiga egenskaperna och sannolikheten för utbredning via direktvåg respektive skuggning. Det visas att den föreslagna skuggningsmodellen kan beskriva det dynamiska beteendet och sannolikheten för att länken är blockerad. Den konstateras också vara adekvat för att modellera och karakterisera kommunikationssystem för millimetervågsbandet med avseende på blockering. Modellens noggrannhet har utvärderats och den anses vara tillfredsställande genom att resultaten validerats mot ett riktmärke som är baserat på verkligt data. Det är möjligt att karakterisera millimetervågskommunikation på systemnivå genom att använda denna modell. Detta verk förser således forskare med en enkel simuleringsbaserad blockeringsmodell som främjar studier och framtagning av kommunikationssystem på millimetervågsbandet.
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Li, Fei. "Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64913.
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Reaction Diffusion Master Equation (RDME) framework, characterized by the discretization of the spatial domain, is one of the most widely used methods in the stochastic simulation of reaction-diffusion systems. Discretization sizes for RDME have to be appropriately chosen such that each discrete compartment is "well-stirred" and the computational cost is not too expensive.
An efficient discretization size based on the reaction-diffusion dynamics of each species is derived in this dissertation. Usually, the species with larger diffusion rate yields a larger discretization size. Partitioning with an efficient discretization size for each species, a multiple grid discretization (MGD) method is proposed. MGD avoids unnecessary molecular jumping and achieves great simulation efficiency improvement.
Moreover, reaction-diffusion systems with reaction dynamics modeled by highly nonlinear functions, show large simulation error when discretization sizes are too small in RDME systems. The switch-like Hill function reduces into a simple bimolecular mass reaction when the discretization size is smaller than a critical value in RDME framework. Convergent Hill function dynamics in RDME framework that maintains the switch behavior of Hill functions
with fine discretization is proposed.
Furthermore, the application of stochastic modeling and simulation techniques to the spatiotemporal regulatory network in Caulobacter crescentus is included. A stochastic model
based on Turing pattern is exploited to demonstrate the bipolarization of a scaffold protein, PopZ, during Caulobacter cell cycle. In addition, the stochastic simulation of the spatiotemporal histidine kinase switch model captures the increased variability of cycle time in cells depleted of the divJ genes.Ph. D.
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Geltz, Brad. "Handling External Events Efficiently in Gillespie's Stochastic Simulation Algorithm." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/168.
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Gillespie's Stochastic Simulation Algorithm (SSA) provides an elegant simulation approach for simulating models composed of coupled chemical reactions. Although this approach can be used to describe a wide variety biological, chemical, and ecological systems, often systems have external behaviors that are difficult or impossible to characterize using chemical reactions alone. This work extends the applicability of the SSA by adding mechanisms for the inclusion of external events and external triggers. We define events as changes that occur in the system at a specified time while triggers are defined as changes that occur to the system when a particular condition is fulfilled. We further extend the SSA with the efficient implementation of these model parameters. This work allows numerous systems that would have previously been impossible or impractical to model using the SSA to take advantage of this powerful simulation technique.
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Venkatachalam, Sangeeta. "Modeling Infectious Disease Spread Using Global Stochastic Field Simulation." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5335/.
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Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, a global stochastic field simulation paradigm (GSFS) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between regions is a function of population density and geographical distance, and has been extended to include demographic and migratory constraints. The progression of diseases using GSFS is analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and similar models of homogeneous population with uniform mixing are addressed by the GSFS model. The GSFS model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSFS model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases.
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Staber, Brian. "Stochastic analysis, simulation and identification of hyperelastic constitutive equations." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1042/document.
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Le projet de thèse concerne la construction, la génération et l'identification de modèles continus stochastiques, pour des milieux hétérogènes exhibant des comportements non linéaires. Le domaine d'application principal visé est la biomécanique, notamment au travers du développement d'outils de modélisation multi-échelles et stochastiques, afin de quantifier les grandes incertitudes exhibées par les tissus mous. Deux aspects sont particulièrement mis en exergue. Le premier point a trait à la prise en compte des incertitudes en mécanique non linéaire, et leurs incidences sur les prédictions des quantités d'intérêt. Le second aspect concerne la construction, la génération (en grandes dimensions) et l'identification multi-échelle de représentations continues à partir de résultats expérimentaux limitésThis work is concerned with the construction, generation and identification of stochastic continuum models, for heterogeneous materials exhibiting nonlinear behaviors. The main covered domains of applications are biomechanics, through the development of multiscale methods and stochastic models, in order to quantify the great variabilities exhibited by soft tissues. Two aspects are particularly highlighted. The first one is related to the uncertainty quantification in non linear mechanics, and its implications on the quantities of interest. The second aspect is concerned with the construction, the generation in high dimension and multiscale identification based on limited experimental data
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Sun, Guangyuan. "Stochastic Simulation of Lagrangian Particle Transport in Turbulent Flows." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5838.
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This dissertation presents the development and validation of the One Dimensional Turbulence (ODT) multiphase model in the Lagrangian reference frame. ODT is a stochastic model that captures the full range of length and time scales and provides statistical information on fine-scale turbulent-particle mixing and transport at low computational cost. The flow evolution is governed by a deterministic solution of the viscous processes and a stochastic representation of advection through stochastic domain mapping processes. The three algorithms for Lagrangian particle transport are presented within the context of the ODT approach. The Type-I and -C models consider the particle-eddy interaction as instantaneous and continuous change of the particle position and velocity, respectively. The Type-IC model combines the features of the Type-I and -C models. The models are applied to the multiphase flows in the homogeneous decaying turbulence and turbulent round jet. Particle dispersion, dispersion coefficients, and velocity statistics are predicted and compared with experimental data. The models accurately reproduces the experimental data sets and capture particle inertial effects and trajectory crossing effect. A new adjustable particle parameter is introduced into the ODT model, and sensitivity analysis is performed to facilitate parameter estimation and selection. A novel algorithm of the two-way momentum coupling between the particle and carrier phases is developed in the ODT multiphase model. Momentum exchange between the phases is accounted for through particle source terms in the viscous diffusion. The source term is implemented in eddy events through a new kernel transformation and an iterative procedure is required for eddy selection. This model is applied to a particle-laden turbulent jet flow, and simulation results are compared with experimental measurements. The effect of particle addition on the velocities of the gas phase is investigated. The development of particle velocity and particle number distribution are illustrated. The simulation results indicate that the model qualitatively captures the turbulent modulation with the presence of difference particle classes with different solid loadings. The model is then extended to simulate temperature evolution of the particles in a nonisothermal hot jet, in which heat transfer between the particles and gas is considered. The flow is bounded by a wall on the one side of the domain. The simulations are performed over a range of particle inertia and thermal relaxation time scales and different initial particle locations. The present study investigates the post-blast-phase mixing between the particles, the environment that is intended to heat them up, and the ambient environment that dilutes the jet flow. The results indicate that the model can qualitatively predict the important particle statistics in jet flame.
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Kekulthotuwage, Don Shamika Prasadini. "Novel mathematical models and simulation tools for stochastic ecosystems." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229974/1/Shamika%20Prasadini_Kekulthotuwage%20Don_Thesis.pdf.
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Interacting species systems have complex dynamics that are often subject to change due to internal and external factors. Quantitative modelling approaches to capture demographic fluctuations can be insufficient in the presence of stochastic variation and uncertainty. This thesis establishes new modelling techniques to account for such demographic variations and develops novel numerical simulation tools for solving these systems. These explorations are extended for solving invasive species management problems where robust management actions and efficient use of allocated budgets are necessary.
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FOSCARI, WIDMANN REZZONICO Piero. "STOCHASTIC AND DETERMINISTIC SIMULATION TECHNIQUES FOR TRAFFIC AND ECONOMICS." Doctoral thesis, Università degli studi di Ferrara, 2009. http://hdl.handle.net/11392/2388713.
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In this work I present the result of different investigations conducted in the last
years in the context of stochastic modeling for decision making in the areas of traffic
simulation and economics.
Traffic simulation has seen us from the Center for Modeling, Computing and
Statistics involved in a project for the evaluation and planning of two highway stretches
in the area around Ferrara. In particular we conducted the modeling and numerical
simulation of the highway network, in collaboration with Michael Herty.
Later the study of kinetic analysis and simulation techniques proved useful in another
related setting, that is agent based models in economics, a discipline of growing
importance in understanding the workings of markets, be they financial or centered
on tangible goods.
Due to my job in the asset management industry some of the research activity has
been tilted towards practical methods for financial simulations, and in particular that
of parallel random number generation is a topic that has been gaining importance
during these last years. While at Eurizon Capital I developed a novel fast algorithm
for moving over certain widely used random number streams, and at NEC Labs Europe
this was further reimplemented as a core block of a professional C++ library for
parallel Monte Carlo simulation in finance.
Finally I present a small note on a common numerical artifact arising in Monte
Carlo simulations when only a limited number of kinetic particles are used. Already
with simple kernels the resulting probability distributions differ significantly from
those predicted by theory and obtained with large particle sets.
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Lundin, Fredrik. "Case studies in omniparametric simulation /." Göteborg : Chalmers University of Technology and Göteborg University, Department of Mathematical Sciences, 2006. http://www.loc.gov/catdir/toc/fy0801/2006411346.html.
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