Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach
(2024)
Journal Article
Brevis, I., Muga, I., Pardo, D., Rodriguez, O., & Van Der Zee, K. G. (2024). Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach. Computers and Mathematics with Applications, 164, 139-149. https://doi.org/10.1016/j.camwa.2024.04.006
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 efficiently learn quantities of interest of solutions to a parametric PDE. The... Read More about Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach.