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/

Zendy Plus

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

Zendy Tools

Zendy Open

We propose the Automatic-differentiated Physics-Informed Echo State Network(API-ESN). The network is constrained by the physical equations through thereservoir's exact time-derivative, which is computed by automaticdifferentiation. As compared to the original Physics-Informed Echo StateNetwork, the accuracy of the time-derivative is increased by up to seven ordersof magnitude. This increased accuracy is key in chaotic dynamical systems,where errors grows exponentially in time. The network is showcased in thereconstruction of unmeasured (hidden) states of a chaotic system. The API-ESNeliminates a source of error, which is present in existing physics-informedecho state networks, in the computation of the time-derivative. This opens upnew possibilities for an accurate reconstruction of chaotic dynamical states.

Automatic-differentiated Physics-Informed Echo State Network (API-ESN)