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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel algorithm, based on physics-informed neural networks(PINNs) to efficiently approximate solutions of nonlinear dispersive PDEs suchas the KdV-Kawahara, Camassa-Holm and Benjamin-Ono equations. The stability ofsolutions of these dispersive PDEs is leveraged to prove rigorous bounds on theresulting error. We present several numerical experiments to demonstrate thatPINNs can approximate solutions of these dispersive PDEs very accurately

Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs