English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The efficient approximation of parametric PDEs is of tremendous importance inscience and engineering. In this paper, we show how one can train Galerkindiscretizations to efficiently learn quantities of interest of solutions to aparametric PDE. The central component in our approach is an efficientneural-network-weighted Minimal-Residual formulation, which, after training,provides Galerkin-based approximations in standard discrete spaces that haveaccurate quantities of interest, regardless of the coarseness of the discretespace.

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