Applying Machine Learning to Study Fluid Mechanics

Applying Machine Learning to Study Fluid Mechanics

5 October 2021

Steven L. Brunton

Papers citing "Applying Machine Learning to Study Fluid Mechanics"

7 / 7 papers shown

Title
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics Rahul Sharma Y.B. Guo M. Raissi W. Guo PINN AI4CE 26 5 0 23 Jul 2023
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks Y. Yasuda R. Onishi 8 4 0 22 Feb 2022
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,254 0 18 Oct 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 121 419 0 10 Mar 2020
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 175 83 0 12 Sep 2019
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 109 355 0 30 Oct 2017