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Machine learning in fluid dynamics: A critical assessment

Kunihiko Taira*

Georgios Rigas†

Kai Fukami‡

  • Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan

  • *Contact author: ktaira@seas.ucla.edu
  • †Contact author: g.rigas@imperial.ac.uk
  • ‡Contact author: kfukami1@tohoku.ac.jp

Extreme aerodynamics: A data-driven perspective

Kunihiko Taira

Phys. Rev. Fluids 11 014702 (2026)

Sharper predictions: The role of loss functions for enhanced turbulent-flow sensing

A. G. Balasubramanian, A. Cremades, R. Vinuesa, and O. Tammisola

Phys. Rev. Fluids 11 044907 (2026)

Interpreting coherence in wall turbulence

Daniele Massaro and Fazle Hussain

Nat Rev Phys 8 276 (2026)

Convolutional causal learning for aerodynamic flows

Ryo Koshikawa, Ryo Araki, Qiong Liu, and Kai Fukami

J. Fluid Mech. 1037 A6 (2026)

Prediction of the drag, lift and torque coefficients of non-spherical particles constrained by wall

Shuo Cheng, Chenghuan He, Jianzhi Yang, Chenyue Xie, Nan-Sheng Liu, and Xi-Yun Lu

J. Fluid Mech. 1034 A40 (2026)

State estimation in homogeneous isotropic turbulence using super-resolution with a 4DVar training algorithm

Markus Weyrauch, Moritz Linkmann, and Jacob Page

J. Fluid Mech. 1032 A43 (2026)

Building a general and data-efficient convolutional neural network-based model for fast urban flow estimation

J. Calafell, J. Bustillo, J.M. Armengol, S. Gómez, F. Ramirez-Javega, and O. Lehmkuhl

Building and Environment 296 114488 (2026)

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