Learning interpretable dynamics of stochastic complex systems from experimental data
Learning interpretable dynamics of stochastic complex systems from experimental data
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Complex systems with many interacting nodes are inherently stochastic and best described by stochastic differential equations. Despite increasing observation data, inferring these equations from empirical data remains challenging. Here, we propose the Langevin graph network approach to learn the hidden stochastic differential equations of complex n...
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Learning interpretable dynamics of stochastic complex systems from experimental data
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TN_cdi_doaj_primary_oai_doaj_org_article_294db17d9fd540a3a9cfa055a348a86b
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_294db17d9fd540a3a9cfa055a348a86b
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-50378-x
