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 introduce a method for training exactly conservative physics-informedneural networks and physics-informed deep operator networks for dynamicalsystems. The method employs a projection-based technique that maps a candidatesolution learned by the neural network solver for any given dynamical systempossessing at least one first integral onto an invariant manifold. Weillustrate that exactly conservative physics-informed neural network solversand physics-informed deep operator networks for dynamical systems vastlyoutperform their non-conservative counterparts for several real-world problemsfrom the mathematical sciences.

Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems