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 Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Mathematical models for complex systems are often accompanied withuncertainties. The goal of this paper is to extract a stochastic differentialequation governing model with observation on stationary probabilitydistributions. We develop a neural network method to learn the drift anddiffusion terms of the stochastic differential equation. We introduce a newloss function containing the Hellinger distance between the observation dataand the learned stationary probability density function. We discover that thelearnt stochastic differential equation provides a fair approximation of thedata-driven dynamical system after minimizing this loss function during thetraining method. The effectiveness of our method is demonstrated in numericalexperiments.

Detecting Stochastic Governing Laws with Observation on Stationary Distributions