Albert Cohen, Sorbonne University
Optimal non-intrusive methods in high-dimension
Motivated by non-intrusive approaches for high-dimensional parametric PDEs, we consider the approximation of an unknown arbirary function in any dimension from the data of point samples, where the approximants are picked from given or adaptively chosen finite dimensional spaces. One principal objective is to obtain an approximation which performs as good as the orthogonal projection using a sampling budget that is linear in the dimension of the approximating space. Using a particular sampling measure, this objective turns out to be met by both least-squares and pseudo-spectral methods in some probabilistic sense, however with some notable distinctions that will be discussed in this talk.