A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs

26 October 2021

Papers citing "A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs"

8 / 8 papers shown

Title
Learning and Transferring Physical Models through Derivatives Alessandro Trenta Andrea Cossu Davide Bacciu AI4CE 31 0 0 02 May 2025
Generating synthetic data for neural operators Erisa Hasani Rachel A. Ward AI4CE 45 7 0 04 Jan 2024
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 30 4 0 04 Aug 2023
Improved architectures and training algorithms for deep operator networks Sifan Wang Hanwen Wang P. Perdikaris AI4CE 42 103 0 04 Oct 2021
Meta-learning PINN loss functions Apostolos F. Psaros Kenji Kawaguchi George Karniadakis PINN 35 96 0 12 Jul 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn Kelvin Xu Sergey Levine BDL 165 666 0 07 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017