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Modern AI versus century-old mathematical models: How far can we go with generative adversarial networks to reproduce stochastic processes?

dc.contributor.authorRego Lencastre e Silva, Pedro
dc.contributor.authorGjersdal, Marit
dc.contributor.authorGorjão, Leonardo Rydin
dc.contributor.authorYazidi, Anis
dc.contributor.authorLind, Pedro
dc.date.accessioned2023-09-04T08:50:11Z
dc.date.available2023-09-04T08:50:11Z
dc.date.created2023-09-01T17:32:11Z
dc.date.issued2023
dc.identifier.issn0167-2789
dc.identifier.urihttps://hdl.handle.net/11250/3087239
dc.language.isoeng
dc.titleModern AI versus century-old mathematical models: How far can we go with generative adversarial networks to reproduce stochastic processes?
dc.title.alternativeModern AI versus century-old mathematical models: How far can we go with generative adversarial networks to reproduce stochastic processes?
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.volume453
dc.source.journalPhysica D : Non-linear phenomena
dc.identifier.doi10.1016/j.physd.2023.133831
dc.identifier.cristin2171764
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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