Optimal sensor placement for active flow control with deep reinforcement learning
Optimal sensor placement for active flow control with deep reinforcement learning
Report on investigation of 3 optimal sensor placement methods for active flow control of the fluidic pinball with deep reinforcement learning. Results have shown that the attention mechanism effectively reduces the number of sensors from 476 to 7 without losing performance, thereby identifying optimal sensors directly in the DRL optimization loop.
Active flow control, reinforcement learning, deep reinforcement learning, optimal sensor placement, CFD, machine learning, deep learning, fluidic pinball, attention, clustering, random forest, proximal policy optimization
Active flow control, reinforcement learning, deep reinforcement learning, optimal sensor placement, CFD, machine learning, deep learning, fluidic pinball, attention, clustering, random forest, proximal policy optimization
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