Perspective on machine learning for advancing fluid mechanics

M. P. Brenner, J. D. Eldredge, J. B. Freund

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number100501
JournalPhysical Review Fluids
Issue number10
StatePublished - Oct 16 2019

ASJC Scopus subject areas

  • Computational Mechanics
  • Modeling and Simulation
  • Fluid Flow and Transfer Processes


Dive into the research topics of 'Perspective on machine learning for advancing fluid mechanics'. Together they form a unique fingerprint.

Cite this