ERC Starting Grant Project HighLEAP (2023-2028)
High-dimensional mathematical methods for LargE Agent and Particle systems
Members
Mathias Dus (postdoc, 1st December 2023 — )
Rodrigue Lelotte (postdoc, 1st December 2023– )
Giulia Sambataro (postdoc, 1st December 2023– )
Description of the project
The Starting Grant ERC project HighLEAP (High-dimensional mathematical methods for LargE Agent and Particle systems) was awarded to Virginie Ehrlacher. The project will last five years from 1st December 2023.
Interacting particle or agent-based systems are ubiquitous in science. They arise in an extremely wide variety of applications including materials science, biology, economics and social sciences. Several mathematical models exist to account for the evolution of such systems at different scales, among which stand optimal transport problems, Fokker-Planck equations, mean-field games systems or stochastic differential equations. However, all of them suffer from severe limitations when it comes to the simulation of high-dimensional problems, the high-dimensionality character coming either from the large number of particles or agents in the system, the high amount of features of each agent or particle, or the huge quantity of parameters entering the model. The objective of this project is to provide a new mathematical framework for the development and analysis of efficient and accurate numerical methods for the simulation of high-dimensional particle or agent systems, stemming from applications in materials science and stochastic game theory.
The main challenges which will be addressed in this project are:
-sparse optimization problems for multi-marginal optimal transport problems, using moment constraints;
-numerical resolution of high-dimensional partial differential equations, with randomized iterative algorithms;
-efficient approximation of parametric stochastic differential equations, by means of reduced-order modeling approaches.
The potential impacts of the project are huge: making possible such extreme-scale simulations will enable to gain precious insights on the predictive power of agent- or particle-based models, with applications in various fields, such as quantum chemistry, molecular dynamics, crowd motion or urban traffic.
Publications
– Mohamed-Raed Blel, Virginie Ehrlacher, Tony Lelièvre, Influence of sampling on the convergence rates of greedy algorithms for parameter-dependent random variables, accepted for publication in Mathematics of Computation, 2024, pdf
– Mathias Dus, Virginie Ehrlacher, Numerical solution of Poisson partial differential equation in high dimension using two-layer neural networks, accepted for publication in Mathematics of Computation, 2024, pdf
