A perspective on machine learning in turbulent flows

Sandeep Pandey, Jörg Schumacher, Katepalli R. Sreenivasan

Research output: Contribution to journalArticlepeer-review


The physical complexity and the large number of degrees of freedom that can be resolved today by direct numerical simulations of turbulent flows, and by the most sophisticated experimental techniques, require new strategies to reduce and analyse the data so generated, and to model the turbulent behaviour. We discuss a few concrete examples for which the turbulence data have been analysed by machine learning tools. We also comment on work in neighbouring fields of physics, particularly astrophysical (and astronomical) work, where Big Data has been the paradigm for some time. We discuss unsupervised, semi-supervised and supervised machine learning methods to direct numerical simulations data of homogeneous isotropic turbulence, Rayleigh-Bénard convection, and the minimal flow unit of a turbulent channel flow; for the last case, we discuss in some detail the application of echo state networks, this being one implementation of reservoir computing. The paper also provides a brief perspective on machine learning applications more broadly.

Original languageEnglish (US)
Pages (from-to)567-584
Number of pages18
JournalJournal of Turbulence
Issue number9-10
StatePublished - Oct 2 2020


  • Fully developed turbulence
  • data-driven turbulence research
  • machine learning

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • General Physics and Astronomy


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