Special issue on machine learning and data-driven methods in fluid dynamics

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

(2020) Suraj Pawar et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Assessment of supervised machine learning methods for fluid flows

(2020) Kai Fukami et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Dimensionality reduction and reduced-order modeling for traveling wave physics

(2020) Ariana Mendible et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Deep model predictive flow control with limited sensor data and online learning

(2020) Katharina Bieker et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

An ensemble Kalman filter approach to parameter estimation for patient-specific cardiovascular flow modeling

(2020) Daniel Canuto et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Data-driven selection of actuators for optimal control of airfoil separation

(2020) Debraj Bhattacharjee et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

(2020) Kazuto Hasegawa et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Enhancement of shock-capturing methods via machine learning

(2020) Ben Stevens et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

An adaptive mesh refinement approach based on optimal sparse sensing

(2020) Daniel Foti et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Data-driven modeling of the chaotic thermal convection in an annular thermosyphon

(2020) Jean-Christophe Loiseau   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

(2020) S. Balachandar et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

Perspective on machine learning for advancing fluid mechanics

(2019) M. P. Brenner et al.   Physical Review Fluids

Turbulence Modeling in the Age of Data

(2018) Karthik Duraisamy et al.   Annual Review of Fluid Mechanics

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

(2018) David Silver et al.   SCIENCE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

A direct numerical simulation study on the mean velocity characteristics in turbulent pipe flow

(2008) XIAOHUA WU et al.   JOURNAL OF FLUID MECHANICS