Machine learning framework for reduced-order modeling and control in fluid–structure interaction. (IMAGE)
Caption
Machine learning framework for reduced-order modeling and control in fluid–structure interaction. This figure illustrates a machine learning framework that compresses high-dimensional fluid–structure interaction (FSI) data into a low-dimensional latent space using an encoder–decoder architecture. Temporal evolution in the latent space enables rapid prediction of flow fields, while a reinforcement learning agent interacts with the environment through local measurements to determine control actions. The model outputs predicted FSI states efficiently, providing a data-driven approach for real-time simulation, flow control, and decision-making in complex FSI systems.
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Ocean, Tsinghua University Press
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