Extension of Physics-Informed Neural Networks to Solving Parameterized PDEs
Abstract
In this paper, we address PINNs’ problem of repetitive and time-consuming training by proposing a novel extension, parameterized physics-informed neural networks (P$^2$INNs). P2INNs enable modeling the solutions of parameterized PDEs via explicitly encoding a latent representation of PDE parameters. With the extensive empirical evaluation, we demonstrate that P$^2$INNs outperform the baselines both in accuracy and parameter efficiency on benchmark 1D and 2D parameterized PDEs and are also effective in overcoming the known “failure modes”.
Cite
Text
Cho et al. "Extension of Physics-Informed Neural Networks to Solving Parameterized PDEs." ICLR 2024 Workshops: AI4DiffEqtnsInSci, 2024.Markdown
[Cho et al. "Extension of Physics-Informed Neural Networks to Solving Parameterized PDEs." ICLR 2024 Workshops: AI4DiffEqtnsInSci, 2024.](https://mlanthology.org/iclrw/2024/cho2024iclrw-extension/)BibTeX
@inproceedings{cho2024iclrw-extension,
title = {{Extension of Physics-Informed Neural Networks to Solving Parameterized PDEs}},
author = {Cho, Woojin and Jo, Minju and Lim, Haksoo and Lee, Kookjin and Lee, Dongeun and Hong, Sanghyun and Park, Noseong},
booktitle = {ICLR 2024 Workshops: AI4DiffEqtnsInSci},
year = {2024},
url = {https://mlanthology.org/iclrw/2024/cho2024iclrw-extension/}
}