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Article: DeepParticle: Learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method

TitleDeepParticle: Learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method
Authors
Wang, ZXin, JZhang, Z
Issue Date2022
Citation
Journal of Computational Physics, 2022, v. 464, p. 111309 How to Cite?
DOI: http://dx.doi.org/10.1016/j.jcp.2022.111309
Persistent Identifierhttp://hdl.handle.net/10722/314574
ISI Accession Number ID
WOS:000807745300007

 

DC FieldValueLanguage
dc.contributor.authorWang, Z-
dc.contributor.authorXin, J-
dc.contributor.authorZhang, Z-
dc.date.accessioned2022-07-22T05:27:13Z-
dc.date.available2022-07-22T05:27:13Z-
dc.date.issued2022-
dc.identifier.citationJournal of Computational Physics, 2022, v. 464, p. 111309-
dc.identifier.urihttp://hdl.handle.net/10722/314574-
dc.languageeng-
dc.relation.ispartofJournal of Computational Physics-
dc.titleDeepParticle: Learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method-
dc.typeArticle-
dc.identifier.emailZhang, Z: zhangzw@hku.hk-
dc.identifier.authorityZhang, Z=rp02087-
dc.identifier.doi10.1016/j.jcp.2022.111309-
dc.identifier.hkuros334461-
dc.identifier.volume464-
dc.identifier.spage111309-
dc.identifier.epage111309-
dc.identifier.isiWOS:000807745300007-

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