Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations
The data-driven discovery of partial differential equations (PDEs) consistent with spatiotemporal data is experiencing a rebirth in machine learning research. Training deep neural networks to learn such data-driven partial differential operators requires extensive spatiotemporal data. For learning c...
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| Format: | Article |
| Language: | English |
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Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/129683 |