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Scalar embeddings reveal that neural network training dynamics can be effectively summarized, preserving critical features like sensitivity to initial conditions and decorrelation times.
KANs show promise in aerodynamic prediction but struggle with stability and hyperparameter sensitivity compared to MLPs and GNNs.
