Modeling concentrated ion mixtures in solvents like water is a complex research area with key applications in biology (e.g., ion transport through protein channels) and electrochemistry (e.g., batteries).

In this talk, I will present a finite volume scheme for modeling the diffusion of ions in constrained geometries using a degenerate Poisson-Nernst-Planck system with size exclusion yielding cross-diffusion. The proposed method utilizes a two-point flux approximation and is part of the exponentially fitted scheme framework. The scheme is shown to be thermodynamically consistent, as it ensures the decay of some discrete version of the free energy. Classical numerical analysis results - existence of discrete solution, convergence of the scheme as the grid size and the time step go to 0 - follow. The long-time behavior of the scheme is also investigated, both from a theoretical and numerical point of view. Numerical simulations confirm our findings, but also point out some possibly very slow convergence towards equilibrium of the system under consideration.

Une grande partie des modèles numériques décrivant des systèmes physiques demandent des simulations nécessitant des grosses capacité de calculs et dépendant de nombreux paramètres. L'objectif de la réduction de modèle est d'utiliser quelques états du système obtenus via des évaluations du modèle numérique afin d'estimer de nouveaux états à moindre coût de calculs. L'une des méthodes permettant cela est la Méthode des Bases Réduites (RBM) : l'idée est d'approcher la variété décrivant l'ensemble des solutions par un espace vectoriel, puis de projeter le problème initial sur ce dernier. Cette méthode permet d’accélérer largement les résolutions de problèmes nécessitant de nombreuses évaluations du modèle numérique (exemples : problèmes inverses, contrôle). En contrepartie, pour assurer la validité des résultats, il est essentiel de disposer d'estimateurs fiables renseignant sur l'erreur introduite par la réduction. Dans notre approche, nous proposons une variation de la RBM qui minimise l'erreur introduite sur les éventuelles mesures du système, un point critique pour la résolution de problèmes inverses.

Cet exposé portera sur une version semi-algébrique du problème de prolongement de Whitney : si une fonction semi-algébrique f:X→ℝ définie sur un fermé X⊂ℝⁿ admet un prolongement F:ℝⁿ→ℝ de classe Cᵐ, alors admet-elle nécessairement un prolongement de classe Cᵐ qui soit aussi semi-algébrique ?

Après quelques rappels sur la géométrie semi-algébrique, je présenterai les résultats positifs connus à ce jour : pour les fonctions de classe C¹ (Aschenbrenner-Thamrongthanyalak), dans le plan (Fefferman-Luli) et pour n et m quelconques mais avec une perte de régularité (travail en collaboration avec E. Bierstone et P. Milman).

I will discuss how superfluidity manifests itself in the spectrum of the Hamiltonian for a test particle travelling through a Bose Einstein condensate. In the Bogoliubov-Fröhlich polaron model, a stable polaron with momentum P corresponds to a ground state of the Hamiltonian at fixed total momentum. I will explain a recent result in collaboration with Benjamin Hinrichs, which shows that a ground state eigenvalue exists if the momentum is less than mc, where m is the particle mass and c is the slope at zero of the dispersion relation of the Bogoliubov phonons.

The spectral theory of the Laplacian on hyperbolic surfaces is a well-studied topic. There are many classical results in various settings (compact, finite volume, infinite volume) on the nature of the spectrum. We will review some of the Theorems on hyperbolic surfaces and mention a few results on higher rank locally symmetric spaces.

We review old and new results on a family of minimization problems, where the minimization space is a set of N orthonormal functions in L2 (the fermions), which interact locally. Such problems arise naturally when we want to optimize a sum of eigenvalues (Lieb-Thirring inequality). We will explain how to show the existence of minimizers for this kind of problem, and give several examples. If time allows, we will display an ad hoc problem where the N = 2 problem is well-posed, but the N = 1 problem has no minimizer.

In this presentation, we tackle the problem of detecting serial correlation in the context of directional data. Motivated by a real data example involving sunspots locations, we define a concept of runs properly adapted to the directional context. We then show that tests based on the latter runs enjoy some local and asymptotic property against local alternatives with serial dependence. We compute the finite sample performances of our tests using Monte Carlo simulations and show their usefulness on a real data illustration that involves the analysis of sunspots locations for various solar cycles. According to the time, we will evoke the goodness of fit problem for the longitude of the location of sunspots (then circular data) using trigonometrics moments.

The objective of extremal regression is to estimate and infer quantities describing the tail of a conditional distribution. Examples of such quantities include quantiles and expectiles, and the regression version of the Expected Shortfall. Traditional regression estimators at the tails typically suffer from instability and inconsistency due to data sparseness, especially when the underlying conditional distributions are heavy-tailed. Existing approaches to extremal regression in the heavy-tailed case fall into two main categories: linear quantile regression approaches and, at the opposite, nonparametric approaches. They are also typically restricted to i.i.d. data-generating processes. I will here give an overview of a recent series of papers that discuss extremal regression methods in location-scale regression models (containing linear regression quantile models) and nonparametric regression models. Some key novel results include a general toolbox for extreme value estimation in the presence of random errors and joint asymptotic normality results for nonparametric extreme conditional quantile estimators constructed upon strongly mixing data. Joint work with A. Daouia, S. Girard, M. Oesting and A. Usseglio-Carleve.

Références : Girard, S., Stupfler, G. and Usseglio-Carleve, A. (2021). Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models, Annals of Statistics 49(6): 3358-3382. Daouia, A., Stupfler, G. and Usseglio-Carleve, A. (2023). Inference for extremal regression with dependent heavy-tailed data, Annals of Statistics 51(5): 2040-2066.