An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations

4 November 2023

Papers citing "An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations"

10 / 10 papers shown

Title
Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs Jean Kossaifi Nikola B. Kovachki Kamyar Azizzadenesheli Anima Anandkumar AI4CE 42 32 0 29 Sep 2023
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 37 24 0 03 Oct 2022
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 108 249 0 11 Jul 2022
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 18 10 0 30 Jun 2022
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks Annan Yu Chloe Becquey Diana Halikias Matthew Esmaili Mallory Alex Townsend 57 8 0 23 Sep 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 272 0 20 Apr 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 2,281 0 18 Oct 2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework C. Jiang S. Esmaeilzadeh Kamyar Azizzadenesheli K. Kashinath Mustafa A. Mustafa H. Tchelepi P. Marcus P. Prabhat Anima Anandkumar AI4CE 182 140 0 01 May 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 756 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 508 0 11 Mar 2020