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.
Reich, A nonparametric ensemble transform method for bayesian inference, SIAM Journal on Scientific Computing 35 (4) (2013) A2013–A2024
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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.