Li, Siran;
Liu, Chong;
Ni, Hao;
(2024)
Enhancing neural operator learning with invariants to simultaneously learn various physical mechanisms.
National Science Review
10.1093/nsr/nwae198.
(In press).
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Abstract
We discuss the recent advancement in PDE learning, focusing on Physics Invariant Attention Neural Operator (PIANO). PIANO is a novel neural operator learning framework for deciphering and integrating physical knowledge from PDEs sampled from multi- physical scenarios.
| Type: | Article |
|---|---|
| Title: | Enhancing neural operator learning with invariants to simultaneously learn various physical mechanisms |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/nsr/nwae198 |
| Publisher version: | http://dx.doi.org/10.1093/nsr/nwae198 |
| Language: | English |
| Additional information: | © The Author(s) 2024. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193524 |
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