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Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
| Content Provider | Semantic Scholar |
|---|---|
| Author | Beucler, Tom Pritchard, Michael Rasp, Stephan Ott, Jordan Baldi, P. Gentine, Pierre |
| Copyright Year | 2019 |
| Abstract | Neural networks can emulate non-linear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints. In this letter, we introduce a systematic way of enforcing analytic constraints in neural networks via constraints in the architecture or the loss function. Applied to the modeling of convective processes for climate modeling, architectural constraints can enforce conservation laws to within machine precision without degrading performance. Furthermore, enforcing constraints can reduce the error of variables closely related to the constraints. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://export.arxiv.org/pdf/1909.00912 |
| Alternate Webpage(s) | https://arxiv.org/pdf/1909.00912v3.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
