The stochastic Landau-Lifshitz-Gilbert equation describes magnetization dynamics in ferromagnetic materials in a thermal bath. In this presentation I discuss the optimal control of a finite spin syste...
Hamilton-Jacobi-Bellman (HJB) equation plays a central role in optimal control and differential games, enabling the computation of robust controls in feedback form. The main disadvantage for this appr...
Physical systems such as gas networks are usually operated in a state of equilibrium and one is interested in stable systems, where small perturbations are damped over time. This talk is devoted to th...
Many interesting applications of hyperbolic systems of equations are stiff, in the sense that restrictive CFL conditions are imposed by fields that one is not really interested in tracking accurately....
In this talk I will present the synthesis of control laws for interacting agent-based dynamics and their mean-field limit. In particular, a linearization-based approach is used for the computation of ...
In this talk, I will introduce a filtering technique for Discontinuous Galerkin approximations of hyperbolic problems. After a brief overview of classical monotonization techniques, I will present an ...
This talk is devoted to the modeling and the stability of multi-lane traffic flow in the microscopic and macroscopic frameworks. First we present the study of a second order microscopic Follow-the-Lea...
This talk is devoted to the numerical approximation of mean field games problems. We consider two cases: a first order problem, i.e the diffusion is null, and a second order problem. For the first one...
We introduce a bilevel problem of the optimal control of an interacting agent system that can be interpreted as a Stackelberg game with a large number of followers. It is shown that the model is well ...
Optimal control and Reinforcement Learning (RL) deal both with sequential decision-making problems, although they use different tools. We have investigated the connection between these two research ar...
In this talk I will present how proteomic, nuclear medicine and imaging data can be used to model the patho-physiology of cancer at different scales, from single cell, through tissues to organs. The m...
Stochastic optimization algorithms are widely employed for problems arising in machine learning but significant issues in their use are open. In fact, tuning these algorithms for each application may ...
