Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is sugg...
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| Published in: | Nature machine intelligence Vol. 3; no. 3; pp. 218 - 229 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
01.03.2021
Nature Publishing Group The Author(s), Springer Nature |
| Subjects: | |
| ISSN: | 2522-5839, 2522-5839 |
| Online Access: | Get full text |
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