Operator compression with deep neural networks
This paper studies the compression of partial differential operators using neural networks. We consider a family of operators, parameterized by a potentially high-dimensional space of coefficients that may vary on a large range of scales. Based on the existing methods that compress such a multiscale...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |