Ces travaux de thèse présentent une étude expérimentale des fluctuations d'une particule Brownienne soumise à deux différentes conditions hors-équilibre dans un fluide . Le but est de comprendre d'une manière générale la relation entre les fluctuations spontanées, la fonction de réponse linéaire et la production totale d'entropie des processus loin de l'équilibre thermique. La première partie est consacrée à l'étude du mouvement d'une particule colloïdale dans un état stationnaire périodique hors-équilibre induit par une force non-conservative et à sa réponse à une perturbation externe. Nous analysons la dynamique du système dans le contexte des différentes approches généralisées de fluctuation-dissipation. Nous montrons que ces relations théoriques sont satisfaites par les données expérimentales quand on prend en compte le rôle du courant du à la rupture du bilan détaillé. Dans une deuxième partie nous étudions les fluctuations et la réponse d'une particule Brownienne dans deux types de bains vieillissants qui relaxent vers l'équilibre thermique: un verre colloïdal de Laponite et une solution aqueuse de gélatine. Dans ce cas-là nous montrons que le flux de chaleur de la particule vers le bain pendant sa relaxation représente une correction hors-équilibre du théorème de fluctuation-dissipation. Donc, le flux de chaleur joue le même rôle que le courant dans un état stationnaire. En conséquence, les résultats de la thèse mettent en évidence l'importance générale de la production totale d'entropie pour quantifier les relations de fluctuation-dissipation généralisées dans les systèmes hors-équilibre.

The topic of this thesis is a diffusion process on a potential landscape which is given by a smooth Hamiltonian function in the regime of small noise. The work provides a new proof of the Eyring-Kramers formula for the Poincaré inequality of the associated generator of the diffusion. The Poincaré inequality characterizes the spectral gap of the generator and establishes the exponential rate of convergence towards equilibrium in the L²-distance. This result was first obtained by Bovier et. al. in 2004 relying on potential theory. The presented approach in the thesis generalizes to obtain also asymptotic sharp estimates of the constant in the logarithmic Sobolev inequality. The optimal constant in the logarithmic Sobolev inequality characterizes the convergence rate to equilibrium with respect to the relative entropy, which is a stronger distance as the L²-distance and slightly weaker than the L¹-distance. The optimal constant has here no direct spectral representation. The proof makes use of the scale separation present in the dynamics. The Eyring-Kramers formula follows as a simple corollary from the two main results of the work: The first one shows that the associated Gibbs measure restricted to a basin of attraction has a good Poincaré and logarithmic Sobolev constants providing the fast convergence of the diffusion to metastable states. The second main ingredient is a mean-difference estimate. Here a weighted transportation distance is used. It contains the main contribution to the Poincaré and logarithmic Sobolev constant, resulting from exponential long waiting times of jumps between metastable states of the diffusion.

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

This thesis describes an experimental study on fluctuations of a Brownian particle immersed in a fluid, confined by optical tweezers and subject to two different kinds of non-equilibrium conditions. We aim to gain a rather general understanding of the relation between spontaneous fluctuations, linear response and total entropy production for processes away from thermal equilibrium. The first part addresses the motion of a colloidal particle driven into a periodic non-equilibrium steady state by a nonconservative force and its response to an external perturbation. The dynamics of the system is analyzed in the context of several generalized fluctuation-dissipation relations derived from different theoretical approaches. We show that, when taking into account the role of currents due to the broken detailed balance, the theoretical relations are verified by the experimental data. The second part deals with fluctuations and response of a Brownian particle in two different aging baths relaxing towards thermal equilibrium: a Laponite colloidal glass and an aqueous gelatin solution. The experimental results show that heat fluxes from the particle to the bath during the relaxation process play the same role of steady state currents as a non-equilibrium correction of the fluctuation-dissipation theorem. Then, the present thesis provides evidence that the total entropy production constitutes a unifying concept which links the statistical properties of fluctuations and the linear response function for non-equilibrium systems either in stationary or non stationary states.

Made available in DSpace on 2014-12-17T15:14:51Z (GMT). No. of bitstreams: 1 JoaoMS.pdf: 634960 bytes, checksum: 579213d21eb24a6a8d342d2c3b15bc3d (MD5) Previous issue date: 2003-06-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / In this work we investigate the stochastic behavior of a large class of systems with variable damping which are described by a time-dependent Lagrangian. Our stochastic approach is based on the Langevin treatment describing the motion of a classical Brownian particle of mass m. Two situations of physical interest are considered. In the first one, we discuss in detail an application of the standard Langevin treatment (white noise) for the variable damping system. In the second one, a more general viewpoint is adopted by assuming a given expression to the so-called collored noise. For both cases, the basic diffententiaql equations are analytically solved and al the quantities physically relevant are explicitly determined. The results depend on an arbitrary q parameter measuring how the behavior of the system departs from the standard brownian particle with constant viscosity. Several types of sthocastic behavior (superdiffusive and subdiffusive) are obteinded when the free pamameter varies continuosly. However, all the results of the conventional Langevin approach with constant damping are recovered in the limit q = 1 / Neste trabalho n?s investigamos o comportamento estoc?stico de uma grande classe de sistemas com amortecimento vari?vel descritos por uma lagraniana dependente do tempo. Nossa abordagem estoc?stica ? baseada no formalismo de Langevin descrevendo o comportamento de uma part?cula browniana cl?ssica de massa m. Duas situa??es de interesse f?sico s?o consideradas. Inicialmente, uma aplica??o do tratamento padr?o de Langevin (ru?do branco) para viscosidade vari?vel ? discutido em detalhe. Na segunda abordagem, um ponto de vista mais geral ? adotado supondo uma dada express?o para o chamado ru?do branco. Em ambos os casos as equa??es diferenciais b?sicas s?o analiticamente resolvidas, e todas as quantidades fisicamente relevantes s?o explicitamente determinadas. Os resultados dependem de um par?metro arbitr?rio (q) medindo como o comportamento din?mico do sistema se afasta daquele apresentado pela part?cula browniana com viscosidade constante. V?rios tipos de comportamentos estoc?sticos (subsifusivos and superdifusivos) s?o obtidos quando o par?metro livre q varia continuamente. Contudo, no limite q -> 1, todos os resultados da abordagem de Langevin convencional s?o recuperados

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Voltage-driven translocation, the core concept of nanopore sensing for biomolecules, has been extensively studied in silico and in vitro over the past two decades. However, the theories of analyte capture are still not complete due to the complex dynamics resulting from the coupling of multiple physical processes such as di usion, electrophoresis, and electroosmotic flow. In this thesis, I build and design translocation simulations for analytes ranging from point-like particles to rod-like molecules and long flexible polymers. The primary goal is to test, clarify and complete the existing capture theories. For example, we revisit and revise the existing definitions of the capture radius, clarify the concept of depletion zones, and investigate the impacts of the flat field near the pore. Earlier theories of translocation underestimate the importance of the electric field out- side the nanopore. In our work, we analyze the non-equilibrium dynamics during the cap- ture process originating from the converging field lines, i.e., rod orientation and polymer deformation. We characterize the rod orientation and quantify its impact on capture time both with and without Electrohydrodynamic interactions. We investigate the polymer chain deformation and calculate the translocation time by taking the electric field outside the nanopore into account as opposed to the conventional simulation approaches. Besides nanopore sensing, there are many undiscovered possibilities for nanopore trans- location technologies. We test two proof-of-concept ideas in which we suggest to use capture and translocation to separate molecules of di erent physical properties. For example, we show how one could selectively capture particles sharing the same mobility but di erent di usion coe cients using a pulsed field. Moreover, we demonstrate that it is possible to build a ratchet using pulsed fields and a nanopore to change the concentration ratios of a polymer mixture of different sized polyelectrolytes.

В работе реализован численный алгоритм для решения уравнения Фоккера-Планка, позволяющий получать значения первой и третьей гармоники ансамбля взаимодействующих частиц для различных амплитуд переменного поля. В формулы первой и третьей гармоники вводились функции, зависящие от параметра и восприимчивости Ланжевена, с неопределенными коэффициентами. Неопределенные коэффициенты находились методом наименьших квадратов. Выражения для функций приближались данными из численного решения уравнения Фоккера-Планка и затем минимизировались относительно неопределённых коэффициентов. Получившиеся формулы сравнивались с численным решением и с известными теориями. / In this work, a numerical algorithm for solving the Fokker-Planck equation, which allows to obtain the values of the first and third harmonics of the ensemble of interacting particles for different amplitudes of the alternating field, was implemented. The functions depending on the parameter and Langevin susceptibility with uncertain coefficients were introduced into the formulas for the first and third harmonics. The uncertain coefficients were found by the least-squares method. Expressions for the functions were approximated with data from the numerical solution of the Fokker-Planck equation and then minimized with respect to the uncertain coefficients. The resulting formulas were compared with the numerical solution and with known theories.

Les modèles génératifs servent à générer des échantillons d'une loi de probabilité (ex. : du texte, des images, de la musique, des vidéos, des molécules, et beaucoup plus) à partir d'un jeu de données (ex. : une banque d'images, de texte, ou autre). Entrainer des modèles génératifs est une tâche très difficile, mais ces outils ont un très grand potentiel en termes d'applications. Par exemple, dans le futur lointain, on pourrait envisager qu'un modèle puisse générer les épisodes d'une émission de télévision à partir d'un script et de voix générés par d'autres modèles génératifs. Il existe plusieurs types de modèles génératifs. Pour la génération d'images, l'approche la plus fructueuse est sans aucun doute la méthode de réseaux adverses génératifs (GANs). Les GANs apprennent à générer des images par un jeu compétitif entre deux joueurs, le Discriminateur et le Générateur. Le Discriminateur tente de prédire si une image est vraie ou fausse, tandis que le Générateur tente de générer des images plus réalistes en apprenant à faire croire au discriminateur que ces fausses images générées sont vraies. En complétant ce jeu, les GANs arrivent à générer des images presque photo-réalistes. Il est souvent possible pour des êtres humains de distinguer les fausses images (générés par les GANs) des vraies images (ceux venant du jeu de données), mais la tâche devient plus difficile au fur et à mesure que cette technologie s'améliore. Le plus gros défaut des GANs est que les données générées par les GANs manquent souvent de diversité (ex. : les chats au visage aplati sont rares dans la banque d'images, donc les GANs génèrent juste des races de chats plus fréquentes). Ces méthodes souvent aussi souvent très instables. Il y a donc encore beaucoup de chemin à faire avant l'obtention d'images parfaitement photo-réalistes et diverses. De nouvelles méthodes telles que les modèles de diffusion à la base de score semblent produire de meilleurs résultats que les GANs, donc tout n'est pas gagné pour les GANs. C'est pourquoi cette thèse n'est pas concentrée seulement sur les GANs, mais aussi sur les modèles de diffusion. Notez que cette thèse est exclusivement concentrée sur la génération de données continues (ex. : images, musique, vidéos) plutôt que discrètes (ex. : texte), car cette dernière fait usage de méthodes complètement différentes. Le premier objectif de cette thèse est d'étudier les modèles génératifs de façon théorique pour mieux les comprendre. Le deuxième objectif de cette thèse est d'inventer de nouvelles astuces (nouvelles fonctions objectives, régularisations, architectures, etc.) permettant d'améliorer les modèles génératifs. Le troisième objectif est de généraliser ces approches au-delà de leur formulation initiale, pour permettre la découverte de nouveaux liens entre différentes approches. Ma première contribution est de proposer un discriminateur relativiste qui estime la probabilité qu'une donnée réelle, soit plus réaliste qu'une donnée fausse (inventée par un modèle générateur). Les GANs relativistes forment une nouvelle classe de fonctions de perte qui apportent beaucoup de stabilité durant l'entrainement. Ma seconde contribution est de prouver que les GANs relativistes forment une mesure de dissimilarité. Ma troisième contribution est de concevoir une variante adverse au appariement de score pour produire des données de meilleure qualité avec les modèles de diffusion. Ma quatrième contribution est d'améliorer la vitesse de génération des modèles de diffusion par la création d'une méthode numérique de résolution pour équations différentielles stochastiques (SDEs). / Generative models are powerful tools to generate samples (e.g., images, music, text) from an unknown distribution given a finite set of examples. Generative models are hard to train successfully, but they have the potential to revolutionize arts, science, and business. These models can generate samples from various data types (e.g., text, images, audio, videos, 3d). In the future, we can envision generative models being used to create movies or episodes from a TV show given a script (possibly also generated by a generative model). One of the most successful methods for generating images is Generative Adversarial Networks (GANs). This approach consists of a game between two players, the Discriminator and the Generator. The goal of the Discriminator is to classify an image as real or fake, while the Generator attempts to fool the Discriminator into thinking that the fake images it generates are real. Through this game, GANs are able to generate very high-quality samples, such as photo-realistic images. Humans are still generally able to distinguish real images (from the training dataset) from fake images (generated by GANs), but the gap is lessening as GANs become better over time. The biggest weakness of GANs is that they have trouble generating diverse data representative of the full range of the data distribution. Thus, there is still much progress to be made before GANs reach their full potential. New methods performing better than GANs are also appearing. One prime example is score-based diffusion models. This thesis focuses on generative models that seemed promising at the time for continuous data generation: GANs and score-based diffusion models. I seek to improve generative models so that they reach their full potential (Objective 1: Improving) and to understand these approaches better on a theoretical level (Objective 2: Theoretical understanding). I also want to generalize these approaches beyond their original setting (Objective 3: Generalizing), allowing the discovery of new connections between different concepts/fields. My first contribution is to propose using a relativistic discriminator, which estimates the probability that a given real data is more realistic than a randomly sampled fake data. Relativistic GANs form a new class of GAN loss functions that are much more stable with respect to optimization hyperparameters. My second contribution is to take a more rigorous look at relativistic GANs and prove that they are proper statistical divergences. My third contribution is to devise an adversarial variant to denoising score matching, which leads to higher quality data with score-based diffusion models. My fourth contribution is to significantly improve the speed of score-based diffusion models through a carefully devised Stochastic Differential Equation (SDE) solver.

<div>The rise of artificial intelligence (AI) hinges on the efficient training of modern deep neural networks (DNNs) for non-convex optimization and uncertainty quantification, which boils down to a non-convex Bayesian learning problem. A standard tool to handle the problem is Langevin Monte Carlo, which proposes to approximate the posterior distribution with theoretical guarantees. However, non-convex Bayesian learning in real big data applications can be arbitrarily slow and often fails to capture the uncertainty or informative modes given a limited time. As a result, advanced techniques are still required.</div><div><br></div><div>In this thesis, we start with the replica exchange Langevin Monte Carlo (also known as parallel tempering), which is a Markov jump process that proposes appropriate swaps between exploration and exploitation to achieve accelerations. However, the na\"ive extension of swaps to big data problems leads to a large bias, and the bias-corrected swaps are required. Such a mechanism leads to few effective swaps and insignificant accelerations. To alleviate this issue, we first propose a control variates method to reduce the variance of noisy energy estimators and show a potential to accelerate the exponential convergence. We also present the population-chain replica exchange and propose a generalized deterministic even-odd scheme to track the non-reversibility and obtain an optimal round trip rate. Further approximations are conducted based on stochastic gradient descents, which yield a user-friendly nature for large-scale uncertainty approximation tasks without much tuning costs. </div><div><br></div><div>In the second part of the thesis, we study scalable dynamic importance sampling algorithms based on stochastic approximation. Traditional dynamic importance sampling algorithms have achieved successes in bioinformatics and statistical physics, however, the lack of scalability has greatly limited their extensions to big data applications. To handle this scalability issue, we resolve the vanishing gradient problem and propose two dynamic importance sampling algorithms based on stochastic gradient Langevin dynamics. Theoretically, we establish the stability condition for the underlying ordinary differential equation (ODE) system and guarantee the asymptotic convergence of the latent variable to the desired fixed point. Interestingly, such a result still holds given non-convex energy landscapes. In addition, we also propose a pleasingly parallel version of such algorithms with interacting latent variables. We show that the interacting algorithm can be theoretically more efficient than the single-chain alternative with an equivalent computational budget.</div>