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 present a machine learning method for model reduction which incorporatesdomain-specific physics through candidate functions. Our method estimates aneffective probability distribution and differential equation model fromstochastic simulations of a reaction network. The close connection betweenreduced and fine scale descriptions allows approximations derived from themaster equation to be introduced into the learning problem. This representationis shown to improve generalization and allows a large reduction in network sizefor a classic model of inositol trisphosphate (IP3) dependent calciumoscillations in non-excitable cells.

Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics