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

We propose an abstract framework for analyzing the convergence ofleast-squares methods based on residual minimization when feasible solutionsare neural networks. With the norm relations and compactness arguments, wederive error estimates for both continuous and discrete formulations ofresidual minimization in strong and weak forms. The formulations cover recentlydeveloped physics-informed neural networks based on strong and variationalformulations.

Error estimates of residual minimization using neural networks for linear PDEs