In this talk we give an overview of some semi-Lagrangian schemes that are applied to the numerical resolution of the Vlasov equation. The latter equation models typically the time evolution of charged...
The idea to represent stochastic processes by orthogonal polynomials has been employed in uncertainty quantification and inverse problems. This approach is known as stochastic Galerkin formulation wit...
For evident reasons, Cancer Biology is one of the most challenging topics of current medical research and understanding the mechanism behind its uncontrolled growth is a crucial issue. Among other exp...
Although traffic models have been extensively studied, obtaining trustful forecast from these models is still challenging, since the evolution of traffic is also exposed to the presence of uncertainti...
Semi-lagrangian schemes for discretization of the dynamic programming principle are based on a time discretization projected on a state-space grid. The use of a structured grid makes this approach not...
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 ...
In this talk, I will describe the main techniques of photographic 3D-reconstruction, which are structure-from-motion, multi-view stereoscopy, shape-from-shading and photometric stereo. I will highligh...
Parabolic partial differential equations are used to describe a wide variety of time-dependent phenomena, arising in a number of important physical problems. The aim of this talk is to present some se...
