期刊
PHYSICAL REVIEW FLUIDS
卷 4, 期 10, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevFluids.4.100501
关键词
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资金
- NSF [DMS-1751477]
- Simons Foundation
- Department of Energy, National Nuclear Security Administration [DE-NA0002374]
A perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.
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