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Optimizing flapping foil dynamics: A data-driven framework for motion optimization
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AI
DOI：10.1016/j.oceaneng.2025.122990.png)
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Abstract
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• We propose a data-driven framework for non-parametric flapping foil optimization, with the objective of performing multi-objective optimization on thrust coefficient and propulsive efficiency. • Our approach combines Gaussian process regression for parametric motion prediction with reinforcement learning for non-parametric motion optimization, yielding motions that outperform the Pareto frontier obtained via brute force search.
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