Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid…

• CN
  Centre National de la Recherche Scientifique
• AF
  Air Force Office of Scientific Research

  Awards: FA9550-, W911NF-17-1-0422, FA9550, AFOSR FA9550-18-1-0200, FA9550-18-1-0200
• AR
  Army Research Office

  Awards: W911NF-17-1-0422, W911NF-17-1-0306, W911NF, ARO W911NF-17-1-0306