KANs achieve good but marginally inferior performance to MLPs for airfoil pressure coefficient prediction while being simpler and faster to train, though unstable and hyperparameter-sensitive; GNNs perform best.
Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries.arXiv preprint arXiv:2408.02950(2024)
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Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs
KANs achieve good but marginally inferior performance to MLPs for airfoil pressure coefficient prediction while being simpler and faster to train, though unstable and hyperparameter-sensitive; GNNs perform best.