Assessment of supervised machine learning methods for fluid flows

Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

(2019) Jean Rabault et al.   JOURNAL OF FLUID MECHANICS

Super-resolution reconstruction of turbulent flows with machine learning

(2019) Kai Fukami et al.   JOURNAL OF FLUID MECHANICS

Predictions of turbulent shear flows using deep neural networks

(2019) P. A. Srinivasan et al.   Physical Review Fluids

Construction of reduced-order models for fluid flows using deep feedforward neural networks

(2019) Hugo F. S. Lui et al.   JOURNAL OF FLUID MECHANICS

Synthetic turbulent inflow generator using machine learning

(2019) Kai Fukami et al.   Physical Review Fluids

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

Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

(2019) Kevin T. Carlberg et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

(2019) Takaaki Murata et al.   JOURNAL OF FLUID MECHANICS

Perspective on machine learning for advancing fluid mechanics

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

Airfoil-Wake Modification with Gurney Flap at Low Reynolds Number

(2018) Muralikrishnan Gopalakrishnan Meena et al.   AIAA JOURNAL

Turbulence Modeling in the Age of Data

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

Flowfield Reconstruction Method Using Artificial Neural Network

(2018) Jian Yu et al.   AIAA JOURNAL

Subgrid modelling for two-dimensional turbulence using neural networks

(2018) R. Maulik et al.   JOURNAL OF FLUID MECHANICS

Inferring flow parameters and turbulent configuration with physics-informed data assimilation and spectral nudging

(2018) Patricio Clark Di Leoni et al.   Physical Review Fluids

Searching for turbulence models by artificial neural network

(2017) Masataka Gamahara et al.   Physical Review Fluids

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

(2015) J. Ling et al.   PHYSICS OF FLUIDS

A few useful things to know about machine learning

(2012) Pedro Domingos   COMMUNICATIONS OF THE ACM

A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions

(2007) Tim Colonius et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING