Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data...
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
About this item
Full title
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
Subjects
More information
Scope and Contents
Contents
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation...
Alternative Titles
Full title
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2730429238
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2730429238
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2210.16110
