Probabilistic neural networks for fluid flow surrogate modeling and data recovery-Reference-Cited by-同舟云学术

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Probabilistic neural networks for fluid flow surrogate modeling and data recovery

Published:2020-10-08 Issue:10 Volume:5 Page:
ISSN:2469-990X
Container-title:Physical Review Fluids
language:en
Short-container-title:Phys. Rev. Fluids

Author:

Maulik Romit,Fukami Kai^ORCID,Ramachandra Nesar,Fukagata Koji^ORCID,Taira Kunihiko^ORCID

Funder

U.S. Department of Energy

Japan Society for the Promotion of Science

Army Research Office

Air Force Office of Scientific Research

Publisher

American Physical Society (APS)

Subject

Fluid Flow and Transfer Processes,Modelling and Simulation,Computational Mechanics

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevFluids.5.104401/fulltext

Reference83 articles.

1. Machine Learning for Fluid Mechanics

2. Perspective on machine learning for advancing fluid mechanics

3. Deep learning in fluid dynamics

4. Assessment of supervised machine learning methods for fluid flows

5. Special issue on machine learning and data-driven methods in fluid dynamics

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