Comparative evaluation of Bayesian Neural Network surrogates versus Gaussian Processes in Bayesian Optimization applied to Carbon Capture and Storage operations, presented as the first such application in reservoir engineering.
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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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Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
Comparative evaluation of Bayesian Neural Network surrogates versus Gaussian Processes in Bayesian Optimization applied to Carbon Capture and Storage operations, presented as the first such application in reservoir engineering.
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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.