Enabling Automatic Differentiation with Mollified Graph Neural Operators

Enabling Automatic Differentiation with Mollified Graph Neural Operators

11 April 2025

Valentin Duruisseaux

Daniel Leibovici

Kamyar Azizzadenesheli

Anima Anandkumar

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Papers citing "Enabling Automatic Differentiation with Mollified Graph Neural Operators"

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Title
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Zekun Shi Zheyuan Hu Min Lin Kenji Kawaguchi 405 8 0 27 Nov 2024
Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators Ha Maust Zong-Yi Li Yixuan Wang Daniel Leibovici O. Bruno T. Hou Anima Anandkumar AI4CE 60 12 0 29 Nov 2022
Neural Conservation Laws: A Divergence-Free Perspective Jack Richter-Powell Y. Lipman Ricky T. Q. Chen 103 56 0 04 Oct 2022
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators Thorsten Kurth Shashank Subramanian P. Harrington Jaideep Pathak Morteza Mardani D. Hall Andrea Miele K. Kashinath Anima Anandkumar AI4Cl 84 192 0 08 Aug 2022
Applications of physics informed neural operators S. Rosofsky Hani Al Majed Eliu A. Huerta PINN AI4CE 44 41 0 23 Mar 2022
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 121 424 0 06 Nov 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 84 468 0 01 Nov 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 82 1,198 0 20 May 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 504 2,448 0 18 Oct 2020
Neural Operator: Graph Kernel Network for Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar 202 748 0 07 Mar 2020
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 91 614 0 28 Nov 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 85 931 0 28 Nov 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 829 11,943 0 09 Mar 2017
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 545 1,412 0 01 Dec 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 172 2,816 0 20 Feb 2015