The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods
| datacite.relatedItem.firstPage | 278 | |
| datacite.relatedItem.issue | 12 | |
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| datacite.relatedItem.relatedItemIdentifier | 2311-5521 | |
| datacite.relatedItem.relationType | IsPublishedIn | |
| datacite.relatedItem.title | Fluids | |
| datacite.relatedItem.volume | 9 | |
| dc.contributor.author | Stefan Heinz | |
| dc.date.accessioned | 2025-04-16T12:52:48Z | |
| dc.date.available | 2025-04-16T12:52:48Z | |
| dc.date.issued | 2024 | |
| dc.identifier.doi | https://doi.org/10.3390/fluids9120278 | |
| dc.identifier.other | jz000104-0268 | |
| dc.identifier.uri | https://tustorage.ulb.tu-darmstadt.de/handle/tustorage/18668 | |
| dc.publisher | MDPI AG | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 530 | |
| dc.title | The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods | |
| dc.type | Article | |
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