A Theoretical Analysis of Deep Neural Networks and Parametric PDEs

Kutyniok, Gitta; Petersen, Philipp; Raslan, Mones und Schneider, Reinhold (2021): A Theoretical Analysis of Deep Neural Networks and Parametric PDEs. In: Constructive Approximation, Bd. 55, Nr. 1: S. 73-125

Abstract

We derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric partial differential equations. In particular, without any knowledge of its concrete shape, we use the inherent low dimensionality of the solution manifold to obtain approximation rates which are significantly superior to those provided by classical neural network approximation results. Concretely, we use the existence of a small reduced basis to construct, for a large variety of parametric partial differential equations, neural networks that yield approximations of the parametric solution maps in such a way that the sizes of these networks essentially only depend on the size of the reduced basis.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Mathematik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISSN: 0176-4276
• Sprache: Englisch