Learning neural operators on Riemannian manifolds
Learning mappings between functions (operators) defined on complex computational domains is a common theoretical challenge in machine learning. Existing operator learning methods mainly focus on regular computational domains, and have many components that rely on Euclidean structural data. However,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press
2024-04-01
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| Series: | National Science Open |
| Subjects: | |
| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20240001 |
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