Mini-Workshop: Nonlinear Approximation of High-dimensional Functions in Scientific Computing

Oberwolfach Reports, vol. 20, pp. 2771–2808

Abstract

Approximation techniques for high dimensional PDEs are crucial for contemporary scientific computing tasks and gained momentum in recent years due to the renewed interest in neural networks. It seems that especially nonlinear parametrizations will play an essential role in efficient and tractable approximations of high dimensional problems. We held a mini-workshop on the relation and possible synergy of neural networks and tensor product approximation. To reliably evaluate the prospect of different numerical experiments, the traditional talks were accompanied by live coding sessions.

Authors 3

1. Mathias Oster Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

   Berlin Philipp Trunschke, Nantes

2. Janina Schütte

   Weierstrass Institute for Applied Analysis and Stochastics

   Affiliation as printed

   Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany

3. Philipp Trunschke

   Nantes Université · Université de Nantes

   Affiliation as printed

   Université de Nantes, France