Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many non...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22598-y |