Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach

Ignacio Brevis, Ignacio Muga, David Pardo +2

The efficient approximation of parametric PDEs is of tremendous importance in science and engineering. In this paper, we show how one can train Galerkin discretizations to efficien…