Mean flow reconstruction of unsteady flows using physics-informed neural networks
Lukasz Sliwinski, Georgios Rigas
Journal
- ISSN / E-ISSN
- — / —
- DOI
- 10.1017/dce.2022.37 ↗
- Volume · Issue · Pages
- 4 · — · —
- Publisher
- Cambridge University Press
- Article URL
- https://doi.org/10.1017/dce.2022.37 ↗
- Article details
- Unverified
Publication date (each date has its type and source)
01 Jan 2023
OpenAlex publication date · source: OpenAlex
| Type | Date | Source |
|---|---|---|
| OpenAlex publication date | 01 Jan 2023 | OpenAlex |
Keywords (as given): physics-informed neural networks; data assimilation; Reynolds-averaged Navier-Stokes; closure models; Reynolds stress; time-averaged velocity; Navier-Stokes equations; pressure field estimation
HEC Status at Publication Not the journal's current status
HEC Status at PublicationNot conclusive
Historical status could not be conclusively determined.
- Some rows of the HEC list for this period are not yet matched to a journal, so absence cannot be proved. Pending verification.
Best available information, not a definitive status: Some rows of the HEC list for this period are not yet matched to a journal, so absence cannot be proved. Pending verification.
- Journal
- Data-Centric Engineering
- Publication date used
- 01 Jan 2023 (OpenAlex publication date)
- Applicable HEC period
- 2022–23 (version 2) · 01 Jul 2022 – 30 Jun 2023
- Category
- —
- Recognition status
- Pending Verification (PERIOD_HAS_UNMATCHED_ROWS)
- Source
- —
- Verification status
- Unverified
This is an automatically generated summary based on the information available on the publication date. Terms & conditions apply.