A FiLM-conditioned DeepONet trained on physics simulations, updated via transfer learning on final experimental deformation, and augmented with Ensemble Kalman Inversion delivers probabilistic time histories of degree of cure, viscosity, and process-induced deformation.
A comprehensive and fair comparison between mlp and kan representations for differential equations and operator networks
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Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network
A FiLM-conditioned DeepONet trained on physics simulations, updated via transfer learning on final experimental deformation, and augmented with Ensemble Kalman Inversion delivers probabilistic time histories of degree of cure, viscosity, and process-induced deformation.
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