Operator Learning on Free-Form Geometries
Abstract
Operator Learning models usually rely on a fixed sampling scheme for training which might limit their ability to generalize to new situations. We present CORAL, a new method which leverages Coordinate-Based Networks for OpeRAtor Learning without any constraints on the training mesh or input sampling. CORAL is able to solve complex Initial Value Problems such as 2D Navier-Stokes or 3D spherical Shallow-Water and can perform zero-shot super-resolution to recover a dense grid, even when the training grid is irregular and sparse. It can also be applied to the task of geometric design with structured or point-cloud data, to infer the steady physical state of a system given the characteristics of the domain.
Cite
Text
Serrano et al. "Operator Learning on Free-Form Geometries." ICLR 2023 Workshops: Physics4ML, 2023.Markdown
[Serrano et al. "Operator Learning on Free-Form Geometries." ICLR 2023 Workshops: Physics4ML, 2023.](https://mlanthology.org/iclrw/2023/serrano2023iclrw-operator/)BibTeX
@inproceedings{serrano2023iclrw-operator,
title = {{Operator Learning on Free-Form Geometries}},
author = {Serrano, Louis and Vittaut, Jean-Noël and Gallinari, Patrick},
booktitle = {ICLR 2023 Workshops: Physics4ML},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/serrano2023iclrw-operator/}
}