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

In this paper we present a data driven approach for approximating dynamicalsystems. A dynamics is approximated using basis functions, which are derivedfrom maximization of the information-theoretic entropy, and can be generateddirectly from the data provided. This approach has advantages over othermethods, where a dictionary of basis functions have to be provided by the user,which is non trivial in some applications. We compare the accuracy of theproposed data-driven modeling approach to existing methods in the literature,and demonstrate that for some applications the maximum entropy basis functionsprovide significantly more accurate models.

Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions