In this dissertation, we present two research projects, namely finite-state mean-field games and the Hughes model for the motion of crowds. In the first part, we describe finite-state mean-field games and some applications to socio-economic sciences. Examples include paradigm shifts in the scientific community and the consumer choice behavior in a free market. The corresponding finite-state mean-field game models are hyperbolic systems of partial differential equations, for which we propose and validate a new numerical method. Next, we consider the dual formulation to two-state mean-field games, and we discuss numerical methods for these problems. We then depict different computational experiments, exhibiting a variety of behaviors, including shock formation, lack of invertibility, and monotonicity loss. We conclude the first part of this dissertation with an investigation of the shock structure for two-state problems. In the second part, we consider a model for the movement of crowds proposed by R. Hughes in [56] and describe a numerical approach to solve it. This model comprises a Fokker-Planck equation coupled with an Eikonal equation with Dirichlet or Neumann data. We first establish a priori estimates for the solutions. Next, we consider radial solutions, and we identify a shock formation mechanism. Subsequently, we illustrate the existence of congestion, the breakdown of the model, and the trend to the equilibrium. We also propose a new numerical method for the solution of Fokker-Planck equations and then to systems of PDEs composed by a Fokker-Planck equation and a potential type equation. Finally, we illustrate the use of the numerical method both to the Hughes model and mean-field games. We also depict cases such as the evacuation of a room and the movement of persons around Kaaba (Saudi Arabia).

We extend sociodynamic modeling of the financial business cycle to the Euro Area and Japan. Using an opinion-formation model and machine learning techniques we find stable model estimation of the financial business cycle using central bank lending surveys and a few selected macroeconomic variables. We find that banks have asymmetric response to good and bad economic information, and that banks adapt to their peers' opinions when changing lending policies.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第21585号 / 理博第4492号 / 新制||理||1645(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 谷村 吉隆, 教授 林 重彦, 教授 寺嶋 正秀 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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L'objectif de notre thèse de <p>doctorat est d’étudier et de décrire les propriétés chimiques et mé- <p>caniques du moteur moléculaire F1 -ATPase. Le moteur F1 -ATPase <p>est un moteur rotatif, d’aspect sphérique et d’environ 10 nanomètre <p>de rayon, qui utilise l’énergie de l’hydrolyse de l’ATP comme car- <p>burant moléculaire. <p>Des questions fondamentales se posent sur le fonctionnement de <p>ce moteurs et sur la quantité de travail qu’il peut fournir. Il s’agit <p>de questions qui concernent principalement la thermodynamique <p>des processus irréversibles. De plus, comme ce moteur est de <p>taille nanométrique, il est fortement influencé par les fluctuations <p>moléculaires, ce qui nécessite une approche stochastique. <p>C’est en créant deux modéles stochastiques complémentaires de <p>ce moteur que nous avons contribué à répondre à ces questions <p>fondamentales. <p>Le premier modèle discuté au chapitre 5 de la thèse, est un mod- <p>èle continu dans le temps et l’espace, décrit par des équations de <p>Fokker-Planck, est construit sur des résultats expérimentaux. <p>Ce modèle tient compte d’une description explicite des fluctua- <p>tions affectant le degré de liberté mécanique et décrit les tran- <p>sitions entre les différents états chimiques discrets du moteur, <p>par un processus de sauts aléatoires entre premiers voisins. Nous <p>avons obtenus des résultats précis concernant la chimie d’hydrolyse <p>et de synthèse de l’ATP, et pour les dépendences du moteur en les <p>différentes variables mécaniques, à savoir, la friction et le couple <p>de force extérieur, ainsi que la dépendence en la température. <p>Les résultats que nous avons obtenus avec ce modèle sont en ex- <p>cellent accord avec les observations expérimentales. <p>Le second modèle est discret dans l’espace et continu dans le <p>temps et est décrit dans le chapitre 6. L’analyse des résultats <p>obtenus par simulations numériques montre que le modèle est <p>en accord avec les observations expérimentales et il permet en <p>outre de dériver des grandeurs thermodynamiques analytique- <p>ment, décrites au chapitre 4, ce que le modèle continu ne permet <p>pas. <p>La comparaison des deux modèles révele la nature du fonction- <p>nement du moteur, ainsi que son régime de fonctionnement loin <p>de l’équilibre. Le second modèle a éte soumis récemment pour <p>publication. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

This thesis is in two parts. The first part considers a theoretical relationship between the natural variability of a stochastic model and its response to a small change in forcing. Over a large enough scale, both the real climate and a climate model are characterised as stochastic dynamical systems. The dynamics of the systems are encoded in the probabilities that the systems move from one state into another. When the systems’ states are discretised and listed, then transition matrices of all these transition probabilities may be formed. The responses of the systems to a small change in forcing are expanded in terms of the eigenfunctions and eigenvalues of the Fokker-Planck equations governing the systems’ transition densities, which may be estimated from the eigenvalues and eigenvectors of the transition matrices. Smoothing the data with a Gaussian kernel improves the estimate of the eigenfunctions, but not the eigenvalues. The significance of differences in two systems’ eigenvalues and eigenfunctions is considered. Three time series from HadCM3 are compared with corresponding series from ERA-40 and the eigenvalues derived from the three pairs of series differ significantly. The second part analyses a model of the coupled climate-economic system, which suggests that the pace of economic growth needs to be reduced and the resilience to climate change needs to be increased in order to avoid a collapse of the human economy. The model condenses the climate-economic system into just three variables: a measure of human wealth, the associated accumulation of greenhouse gases, and the consequent level of global warming. Global warming is assumed to dictate the pace of economic growth. Depending on the sensitivity of economic growth to global warming, the model climate-economy system either reaches an equilibrium or oscillates in century-scale booms and busts.

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

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

Thesis (Ph.D.); Submitted to the University of California at Berkeley, Berkeley, CA 94720 (US); 1 Dec 2004. / Published through the Information Bridge: DOE Scientific and Technical Information. "LBNL--56842" Barber IV, John Letherman. USDOE Director. Office of Science. Advanced Scientific Computing Research (US) 12/01/2004. Report is also available in paper and microfiche from NTIS.

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