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 study focuses on addressing the challenges of solving analyticallyintractable differential equations that arise in scientific and engineeringfields such as Hamilton-Jacobi-Bellman. Traditional numerical methods andneural network approaches for solving such equations often require independentsimulation or retraining when the underlying parameters change. To overcomethis, this study employs a physics-informed DeepONet (PI-DeepONet) toapproximate the solution operator of a nonlinear parabolic equation.PI-DeepONet integrates known physics into a deep neural network, which learnsthe solution of the PDE.

Learning the solution operator of a nonlinear parabolic equation using physics informed deep operator network