Generating synthetic data for neural operators

4 January 2024

Papers citing "Generating synthetic data for neural operators"

31 / 31 papers shown

Title
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Lothar Heimbach Sebastian Kaltenbach Petr Karnakov Francis J. Alexander Petros Koumoutsakos AI4CE 113 0 0 16 May 2025
Data-Efficient Inference of Neural Fluid Fields via SciML Foundation Model Yuqiu Liu Jingxuan Xu Mauricio Soroco Yunchao Wei Wuyang Chen AI4CE 122 2 0 18 Dec 2024
Neural Green's Operators for Parametric Partial Differential Equations Hugo Melchers Joost Prins Michael Abdelmalik 150 4 0 04 Jun 2024
MODNO: Multi Operator Learning With Distributed Neural Operators Zecheng Zhang 94 8 0 03 Apr 2024
Neural Parameter Regression for Explicit Representations of PDE Solution Operators Konrad Mundinger Max Zimmer Sebastian Pokutta 85 1 0 19 Mar 2024
Neural Operators with Localized Integral and Differential Kernels Miguel Liu-Schiaffini Julius Berner Boris Bonev Thorsten Kurth Kamyar Azizzadenesheli A. Anandkumar 86 23 0 26 Feb 2024
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning Wuyang Chen Jialin Song Pu Ren Shashank Subramanian Dmitriy Morozov Michael W. Mahoney AI4CE 90 12 0 24 Feb 2024
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior Shashank Subramanian P. Harrington Kurt Keutzer W. Bhimji Dmitriy Morozov Michael W. Mahoney A. Gholami AI4CE 87 77 0 01 Jun 2023
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F. Pichi B. Moya J. Hesthaven AI4CE 60 56 0 15 May 2023
LNO: Laplace Neural Operator for Solving Differential Equations Qianying Cao S. Goswami George Karniadakis 68 48 0 19 Mar 2023
Convolutional Neural Operators for robust and accurate learning of PDEs Bogdan Raonić Roberto Molinaro Tim De Ryck Tobias Rohner Francesca Bartolucci Rima Alaifari Siddhartha Mishra Emmanuel de Bezenac AAML 104 94 0 02 Feb 2023
Neural Inverse Operators for Solving PDE Inverse Problems Roberto Molinaro Yunan Yang Bjorn Engquist Siddhartha Mishra AI4CE 56 40 0 26 Jan 2023
Random Grid Neural Processes for Parametric Partial Differential Equations Arnaud Vadeboncoeur Ieva Kazlauskaite Y. Papandreou F. Cirak Mark Girolami Ömer Deniz Akyildiz AI4CE 63 11 0 26 Jan 2023
Non-equispaced Fourier Neural Solvers for PDEs Haitao Lin Lirong Wu Yongjie Xu Yufei Huang Siyuan Li Guojiang Zhao Z. Stan 52 8 0 09 Dec 2022
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 169 268 0 11 Jul 2022
NOMAD: Nonlinear Manifold Decoders for Operator Learning Jacob H. Seidman Georgios Kissas P. Perdikaris George J. Pappas AI4CE 72 75 0 07 Jun 2022
GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations Onur Bilgin Thomas Vergutz S. Mehrkanoon GNN 47 3 0 28 Apr 2022
U-NO: U-shaped Neural Operators Md Ashiqur Rahman Zachary E. Ross Kamyar Azizzadenesheli AI4CE 107 144 0 23 Apr 2022
Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions Lesley Tan Liang Chen 39 13 0 17 Feb 2022
Factorized Fourier Neural Operators Alasdair Tran A. Mathews Lexing Xie Cheng Soon Ong AI4CE 71 155 0 27 Nov 2021
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby PINN AI4CE 79 42 0 26 Oct 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 117 449 0 19 Aug 2021
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs Lulu Zhang Yaoyu Zhang Yaoyu Zhang Weinan E Z. Xu Zheng Ma AI4CE 50 33 0 08 Jul 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 97 699 0 19 Mar 2021
Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang K. Garikipati AI4CE 67 12 0 13 Jan 2021
Multipole Graph 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 211 392 0 16 Jun 2020
Model Reduction and Neural Networks for Parametric PDEs K. Bhattacharya Bamdad Hosseini Nikola B. Kovachki Andrew M. Stuart 212 332 0 07 May 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 196 736 0 07 Mar 2020
PDE-Net: Learning PDEs from Data Zichao Long Yiping Lu Xianzhong Ma Bin Dong DiffM AI4CE 43 756 0 26 Oct 2017
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 121 1,387 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 91 2,063 0 24 Aug 2017