dc.contributor.author | Rego Lencastre e Silva, Pedro | |
dc.contributor.author | Gjersdal, Marit | |
dc.contributor.author | Gorjão, Leonardo Rydin | |
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Lind, Pedro | |
dc.date.accessioned | 2023-09-04T08:50:11Z | |
dc.date.available | 2023-09-04T08:50:11Z | |
dc.date.created | 2023-09-01T17:32:11Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0167-2789 | |
dc.identifier.uri | https://hdl.handle.net/11250/3087239 | |
dc.language.iso | eng | |
dc.title | Modern AI versus century-old mathematical models: How far can we go with generative adversarial networks to reproduce stochastic processes? | |
dc.title.alternative | Modern AI versus century-old mathematical models: How far can we go with generative adversarial networks to reproduce stochastic processes? | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.source.volume | 453 | |
dc.source.journal | Physica D : Non-linear phenomena | |
dc.identifier.doi | 10.1016/j.physd.2023.133831 | |
dc.identifier.cristin | 2171764 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |