Optimization of fluid control laws through deep reinforcement learning using dynamic mode decomposition as the environment
The optimization of fluid control laws through deep reinforcement learning (DRL) presents a challenge owing to the considerable computational costs associated with trial-and-error processes. In this study, we examine the feasibility of deriving an effective control law using a reduced-order model co...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2024-11-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0237682 |
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