Scientific machine learning for closure models in multiscale problems: A review

2025-03-01

Foundations of Data Science , Volume 7 - Issue 1 p. 298- 337

Additional Metadata
Keywords	Closure model, Scientific machine learning, Multiscale problem, Turbulence, Parameterization, Model-data consistency, Large eddy simulation, Physics-constrained learning, Differentiable physics
Persistent URL	doi.org/10.3934/fods.2024043
Journal	Foundations of Data Science
Project	Unravelling Neural Networks with Structure-Preserving Computing , Discretize first, reduce next: a new paradigm to closure for fluid flow simulation
Grant	This work was funded by the NWO; grant id nwo/VI.Vidi.193.105 - Discretize first, reduce next: a new paradigm to closure for fluid flow simulation
Organisation	Scientific Computing
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Sanderse, B., Stinis, P., Maulik, R., & Ahmed, S. (2025). Scientific machine learning for closure models in multiscale problems: A review. Foundations of Data Science, 7(1), 298–337. doi:10.3934/fods.2024043

See Also
techReport Scientific machine learning for closure models in multiscale problems: A review B. Sanderse (Benjamin), P. Stinis (Panos), R. Maulik (Romit) and S.E. Ahmed (Shady)

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