Deep Operator Networks predict composite material deformation with uncertainty quantification

Researchers have developed a Deep Operator Network (DeepONet) to predict process-induced deformation in carbon/epoxy composites. This data-driven surrogate model combines physics-based simulations with experimental measurements to account for thermal expansion and cure shrinkage. The study also incorporates transfer learning and Ensemble Kalman Inversion (EKI) to improve prediction accuracy and quantify uncertainty, aiding in the optimization of manufacturing processes. AI

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IMPACT Introduces a novel DeepONet application for material science, potentially improving manufacturing process optimization.

RANK_REASON This is a research paper detailing a novel application of Deep Operator Networks for predicting material deformation.

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• Deep Operator Network
• DeepONet
• Ensemble Kalman Inversion
• EKI
• AS4 carbon fiber
• epoxy

• paper
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