<ul><li>Reinforcement learning (RL) has shown potential in learning complex control strategies for active flow control tasks.</li><li>However, RL applications in turbulent flows are computationally challenging and have limited generalization capabilities.</li><li>To address these limitations, this work proposes the use of graph neural networks (GNNs) for active flow control.</li><li>The results demonstrate that GNN-based control policies achieve comparable performance and improved generalization properties.</li></ul>

Invariant Control Strategies for Active Flow Control using Graph Neural Networks

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