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

This research presents the development of an innovative algorithm tailoredfor the adaptive sampling of residual points within the framework ofPhysics-Informed Neural Networks (PINNs). By addressing the limitationsinherent in existing adaptive sampling techniques, our proposed methodologyintroduces a direct mesh refinement approach that effectively ensures bothcomputational efficiency and adaptive point placement. Verification studieswere conducted to evaluate the performance of our algorithm, showcasingreasonable agreement between the model based on our novel approach andbenchmark model results. Comparative analyses with established adaptiveresampling techniques demonstrated the superior performance of our approach,particularly when implemented with higher refinement factor. Overall, ourfindings highlight the enhancement of simulation accuracy achievable throughthe application of our adaptive sampling algorithm for Physics-Informed NeuralNetworks.

Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm