Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators
Abstract To understand and predict large, complex, and chaotic systems, Earth scientists build simulators from physical laws. Simulators generalize better to new scenarios, require fewer tunable parameters, and are more interpretable than nonphysical deep learning, but procedures for obtaining their...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2021-06-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021MS002554 |