Metadata only
Date
2023Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
A large class of inverse problems for PDEs are only well-defined as mappings from operators to functions. Existing operator learning frameworks map functions to functions and need to be modified to learn inverse maps from data. We propose a novel architecture termed Neural Inverse Operators (NIOs) to solve these PDE inverse problems. Motivated by the underlying mathematical structure, NIO is based on a suitable composition of DeepONets and FNOs to approximate mappings from operators to functions. A variety of experiments are presented to demonstrate that NIOs significantly outperform baselines and solve PDE inverse problems robustly, accurately and are several orders of magnitude faster than existing direct and PDE-constrained optimization methods. Show more
Publication status
publishedExternal links
Editor
Book title
Proceedings of the 40th International Conference on Machine LearningJournal / series
Proceedings of Machine Learning ResearchVolume
Pages / Article No.
Publisher
PMLREvent
Organisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Related publications and datasets
Is new version of: http://hdl.handle.net/20.500.11850/596104
More
Show all metadata
ETH Bibliography
yes
Altmetrics