CEMRACS 2021 - Mathematical and Computational Methods for High-Dimensional Problems

Explore advanced mathematical and computational methods through this comprehensive conference series featuring nine expert lectures on cutting-edge topics in applied mathematics and data science. Delve into transition time computations with Tony Lelièvre, master Bayesian data assimilation and filtering techniques through Claudia Schillings' instruction, and understand the statistical foundations of deep neural networks via Johannes Schmidt-Hieber's two-part presentation. Learn about innovative model coupling approaches from Olivier Lafitte, discover approximation and learning methodologies using tree tensor networks in Anthony Nouy's dual-lecture series, examine high-dimensional function approximation theory with Albert Cohen, and investigate Bayesian approaches to inverse problems through Massoumeh Dashti's expertise. Gain insights into the intersection of mathematics, statistics, and computational science through these research-level presentations delivered by leading experts in their respective fields.

Syllabus

Tony Lelièvre: How to compute transition times?
Claudia Schillings: Bayesian data assimilation and filtering - lecture 2
Johannes Schmidt-Hieber: Statistical theory for deep neural networks - lecture 2
Johannes Schmidt-Hieber: Statistical theory for deep neural networks - lecture 1
Olivier Lafitte: Coupling models instead of coupling codes: two toy examples
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 2
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 1
Albert Cohen: Theory of approximation of hight-dimensional functions - lecture 2
Massoumeh Dashti: Bayesian methods for inverse problems - lecture 2