Phy-Taylor: Physics-Model-Based Deep Neural Networks

27 September 2022

Papers citing "Phy-Taylor: Physics-Model-Based Deep Neural Networks"

8 / 8 papers shown

Title
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 489 0 09 Feb 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 2,272 0 18 Oct 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 144 369 0 09 May 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 80 385 0 10 Mar 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 121 419 0 10 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction Vincent Le Guen Nicolas Thome AI4CE PINN 78 287 0 03 Mar 2020
Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving Grady Williams P. Drews Brian Goldfain James M. Rehg Evangelos A. Theodorou 76 260 0 07 Jul 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,809 0 25 Nov 2016