Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning

30 January 2023

Papers citing "Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning"

8 / 8 papers shown

Title
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 54 1 0 10 Feb 2025
Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs Hrishikesh Viswanath Yue Chang Julius Berner Peter Yichen Chen Aniket Bera Aniket Bera AI4CE 63 2 0 04 Jul 2024
An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator A. Mavi A. Bekar E. Haghighat E. Madenci 57 28 0 21 Oct 2022
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 119 251 0 11 Jul 2022
Variable-Input Deep Operator Networks Michael Prasthofer Tim De Ryck Siddhartha Mishra 42 23 0 23 May 2022
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 126 200 0 28 Sep 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 208 2,287 0 18 Oct 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 329 11,684 0 09 Mar 2017