Institut de recherche mathématique avancée

Résumé : Considérons un système de particules aléatoires. quand peut-on dire qu'il est proche de l'équilibre ? Parfois, le système atteint rapidement l'équilibre de manière abrupte, ce que l'on qualifie de phénomène de cut-off. Le but de l'exposé est de présenter ce phénomène et d'expliquer quels renseignements fournit le comportement macroscopique d'un système sur le temps de mélange.

Résumé : Dans cette présentation, je rappellerai quelques résultats essentiels donnant des critères de compacité dans des espaces fonctionnels fondamentaux. Dans un second temps, nous verrons comment décrire la perte de compacité dans des espaces bien connus des analystes et edpistes, que sont les espaces de Sobolev.

Résumé : This work is devoted to the study of dimension reduction techniques for multivariate spatially indexed functional data and defined on different domains. We present a method called Spatial Multivariate Functional Principal Component Analysis (SMFPCA), which performs principal component analysis for multivariate spatial functional data. In contrast to Multivariate Karhunen- Loève approach for independent data, SMFPCA is notably adept at effectively capturing spatial dependencies among multiple functions. SMFPCA applies spectral functional component analysis to multivariate functional spatial data, focusing on data points arranged on a regular grid. The methodological framework and algorithm of SMFPCA have been developed to tackle the challenges arising from the lack of appropriate methods for managing this type of data. The performance of the proposed method has been verified through finite sample properties using simulated datasets and sea-surface temperature dataset. Additionally, we conducted comparative studies of SMFPCA against some existing methods providing valuable insights into the properties of multivariate spatial functional data within a finite sample.

Résumé : I will discuss a microlocal analysis approach to spectral theory on asymptotically Minkowski spaces both for scalar wave operators and also for Dirac type operators. This in turn gives rise to complex powers of the operators, allowing for the analysis of a spectral zeta function, relating its residues to geometric information. This is joint work with Nguyen Viet Dang and Michal Wrochna, with ongoing work on extensions also with Mikhail Molodyk.

Résumé : Piecewise Deterministic Markov Processes (PDMPs) constitute a family of Markov processes characterized by deterministic motion interspersed with random jumps. When controlled through discrete-time interventions, this leads to an impulse control problem. In the fully observed setting with known dynamics we develop a numerical method to compute an optimal strategy. In real-world applications, however, full observability is rarely available. Under partial observation, the impulse control of a PDMP can be reformulated as a Partially Observed Markov Decision Process (POMDP), which we address using deep reinforcement learning techniques. A major limitation of existing approaches is the assumption that the underlying PDMP dynamics are known or can be accurately simulated. This assumption is unrealistic in applications such as patient monitoring, where data may be scarce and disease dynamics may vary across individuals. To address this issue, we introduce a Bayesian Adaptive POMDP (BAPOMDP) framework, in which the unknown PDMP parameters are modeled probabilistically and updated through Bayesian inference. The resulting continuous-state BAPOMDP is solved using deep reinforcement learning methods adapted to high-dimensional belief spaces. This work thus combines stochastic control theory, Bayesian modeling, and deep reinforcement learning to provide a unified framework for decision-making under partial observability and model uncertainty. The proposed methodology is thoroughly illustrated and validated on a medical application : the adaptive follow-up and monitoring of patients diagnosed with multiple myeloma.