Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
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Ithaca: Cornell University Library, arXiv.org
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Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predicti...
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Full title
Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
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TN_cdi_proquest_journals_2666713023
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2666713023
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2205.08663
