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Perspective on machine learning for advancing fluid mechanics

M. P. Brenner*

J. D. Eldredge†

J. B. Freund‡

  • School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA and Google Research, Mountain View, California 94103, USA

  • Mechanical and Aerospace Engineering, University of California–Los Angeles, Los Angeles, California 90095, USA

  • Mechanical Science & Engineering and Aerospace Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, USA

  • *mpbrenner@gmail.com
  • †jdeldre@ucla.edu
  • ‡jbfreund@illinois.edu
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Phys. Rev. Fluids 4, 100501 – Published 16 October, 2019

DOI: https://doi.org/10.1103/PhysRevFluids.4.100501

Abstract

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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Phys. Rev. Fluids 4, 100501– Published 16 October, 2019
  • Received 12 June 2019
Article part of CHORUS
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