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.
Köppen, On the training of a kolmogorov network, in: Artificial Neural Networks—ICANN 2002: International Conference, Madrid, Spain, August 28–30, 2002, 2022, pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CE 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.