Generic bounds on the approximation error for physics-informed (and) operator learning

23 May 2022

Papers citing "Generic bounds on the approximation error for physics-informed (and) operator learning"

5 / 5 papers shown

Title
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 28 0 0 02 Mar 2025
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning Tyler Chang Andrew Gillette R. Maulik 19 2 0 04 Apr 2024
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 31 23 0 03 Oct 2022
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 188 2,254 0 18 Oct 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 161 616 0 13 Mar 2020