ModalPINN: An extension of physics-informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors

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

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

Machine Learning for Fluid Mechanics

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

Physics-informed neural networks for high-speed flows

(2019) Zhiping Mao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Efficient collective swimming by harnessing vortices through deep reinforcement learning

(2018) Siddhartha Verma et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning of vortex-induced vibrations

(2018) Maziar Raissi et al.   JOURNAL OF FLUID MECHANICS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

hp-Adaptive time integration based on the BDF for viscous flows

(2015) A. Hay et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Inter-blade flow analysis of a propeller turbine runner using stereoscopic PIV

(2013) Vincent Aeschlimann et al.   EUROPEAN JOURNAL OF MECHANICS B-FLUIDS

On the uncertainty of digital PIV and PTV near walls

(2012) Christian J. Kähler et al.   EXPERIMENTS IN FLUIDS

Perspective on the geometric conservation law and finite element methods for ALE simulations of incompressible flow

(2008) S. Étienne et al.   JOURNAL OF COMPUTATIONAL PHYSICS