A variational approach to modeling slow processes in stochastic dynamical systems

The slow processes of metastable stochastic dynamical systems are difficult to access by direct numerical simulation due the sampling problem. Here, we suggest an approach for modeling the slow parts of Markov processes by approximating the dominant eigenfunctions and eigenvalues of the propagator. To this end, a variational principle is derived that is based on the maximization of a Rayleigh coefficient. It is shown that this Rayleigh coefficient can be estimated from statistical observables that can be obtained from short distributed simulations starting from different parts of state space. The approach forms a basis for the development of adaptive and efficient computational algorithms for simulating and analyzing metastable Markov processes while avoiding the sampling problem. Since any stochastic process with finite memory can be transformed into a Markov process, the approach is applicable to a wide range of processes relevant for modeling complex real-world phenomena

Towards tensor-based methods for the numerical approximation of the Perron-Frobenius and Koopman operator

The global behavior of dynamical systems can be studied by analyzing the eigenvalues and corresponding eigenfunctions of linear operators associated with the system. Two important operators which are frequently used to gain insight into the system's behavior are the Perron-Frobenius operator and the Koopman operator. Due to the curse of dimensionality, computing the eigenfunctions of high-dimensional systems is in general infeasible. We will propose a tensor-based reformulation of two numerical methods for computing finite-dimensional approximations of the aforementioned infinite-dimensional operators, namely Ulam's method and Extended Dynamic Mode Decomposition (EDMD). The aim of the tensor formulation is to approximate the eigenfunctions by low-rank tensors, potentially resulting in a significant reduction of the time and memory required to solve the resulting eigenvalue problems, provided that such a low-rank tensor decomposition exists. Typically, not all variables of a high-dimensional dynamical system contribute equally to the system's behavior, often the dynamics can be decomposed into slow and fast processes, which is also reflected in the eigenfunctions. Thus, the weak coupling between different variables might be approximated by low-rank tensor cores. We will illustrate the efficiency of the tensor-based formulation of Ulam's method and EDMD using simple stochastic differential equations

Effective dynamics for a kinetic Monte-Carlo model with slow and fast time scales

We consider several multiscale-in-time kinetic Monte Carlo models, in which some variables evolve on a fast time scale, while the others evolve on a slow time scale. In the first two models we consider, a particle evolves in a one-dimensional potential energy landscape which has some small and some large barriers, the latter dividing the state space into metastable regions. In the limit of infinitely large barriers, we identify the effective dynamics between these macro-states, and prove the convergence of the process towards a kinetic Monte Carlo model. We next consider a third model, which consists of a system of two particles. The state of each particle evolves on a fast time-scale while conserving their respective energy. In addition, the particles can exchange energy on a slow time scale. Considering the energy of the first particle, we identify its effective dynamics in the limit of asymptotically small ratio between the characteristic times of the fast and the slow dynamics. For all models, our results are illustrated by representative numerical simulations

Pseudo generators of spatial transfer operators

Metastable behavior in dynamical systems may be a significant challenge for a simulation based analysis. In recent years, transfer operator based approaches to problems exhibiting metastability have matured. In order to make these approaches computationally feasible for larger systems, various reduction techniques have been proposed: For example, Sch\"utte introduced a spatial transfer operator which acts on densities on configuration space, while Weber proposed to avoid trajectory simulation (like Froyland et al.) by considering a discrete generator. In this manuscript, we show that even though the family of spatial transfer operators is not a semigroup, it possesses a well defined generating structure. What is more, the pseudo generators up to order 4 in the Taylor expansion of this family have particularly simple, explicit expressions involving no momentum averaging. This makes collocation methods particularly easy to implement and computationally efficient, which in turn may open the door for further efficiency improvements in, e.g., the computational treatment of conformation dynamics. We experimentally verify the predicted properties of these pseudo generators by means of two academic examples

A direction preserving discretization for computing phase-space densities

Ray flow methods are an efficient tool to estimate vibro-acoustic or electromagnetic energy transport in complex domains at high-frequencies. Here, a Petrov--Galerkin discretization of a phase-space boundary integral equation for transporting wave energy densities on two-dimensional surfaces is proposed. The directional dependence of the energy density is approximated at each point on the boundary in terms of a finite local set of directions propagating into the domain. The direction of propagation can be preserved for transport across multicomponent domains when the directions within the local set are inherited from a global direction set. The range of applicability and computational cost of the method will be explored through a series of numerical experiments, including wave problems from both acoustics and elasticity in both single and multicomponent domains. The domain geometries considered range from both regular and irregular polygons to curved surfaces, including a cast aluminium shock tower from a Range Rover car

Etude mathématique de modèles quantiques et classiques pour les matériaux aléatoires à l'échelle atomique

Les contributions de cette thèse portent sur deux sujets.La première partie est dédiée à l'étude de modèles de champ moyen pour la structure électronique de matériaux avec des défauts.Dans le chapitre~ref{chap:ergodic_crystals}, nous introduisons et étudions le modèle de Hartree-Fock réduit (rHF) pour des cristaux désordonnés. Nous prouvons l'existence d'un état fondamental et établissons, pour les interactions de Yukawa (à courte portée), certaines propriétés de cet état. Dans le chapitre~ref{chap:défauts_étendus}, nous considérons des matériaux avec des défauts étendus. Dans le cas des interactions de Yukawa, nous prouvons l'existence d'un état fondamental, solution de l'équation auto-cohérente. Nous étudions également le cas de cristaux avec une faible concentration de défauts aléatoires. Dans le chapitre~ref{chap:numerical_simuation}, nous présentons des résultats de simulations numériques de systèmes aléatoires en dimension un.Dans la deuxième partie, nous étudions des modèles Monte-Carlo cinétique multi-échelles en temps. Nous prouvons, pour les trois modèles présentés au chapitre~ref{chap:kMC}, que les variables lentes convergent, dans la limite de la grande séparation des échelles de temps, vers une dynamique effective. Nos résultats sont illustrés par des simulations numériques.The contributions of this thesis concern two topics.The first part is dedicated to the study of mean-field models for the electronic structure of materials with defects. In Chapter~ref{chap:ergodic_crystals}, we introduce and study the reduced Hartree-Fock (rHF) model for disordered crystals. We prove the existence of a ground state and establish, for (short-range)Yukawa interactions, some properties of this ground state. In Chapter~ref{chap:défauts_étendus}, we consider crystals with extended defects. Assuming Yukawa interactions, we prove the existence of an electronic ground state, solution of the self-consistent field equation. We also investigate the case of crystals with low concentration of random defects. In Chapter~ref{chap:numerical_simuation}, we present some numerical results obtained from the simulation of one-dimensional random systems.In the second part, we consider multiscale-in-time kinetic Monte Carlo models. We prove, for the three models presented in Chapter~ref{chap:kMC}, that in the limit of large time-scale separation, the slow variables converge to an effective dynamics. Our results are illustrated by numerical simulations.CERGY PONTOISE-Bib. electronique (951279901) / SudocSudocFranceF