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Deep Operator Networks predict composite material deformation with uncertainty quantification

By PulseAugur Editorial · [1 sources] ·

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

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.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Operator Networks predict composite material deformation with uncertainty quantification

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This is a research paper detailing a novel application of Deep Operator Networks for predicting material deformation.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Elham Kiyani, Amit Makarand Deshpande, Madhura Limaye, Zhiwei Gao, Zongren Zou, Sai Aditya Pradeep, Srikanth Pilla, Gang Li, Zhen Li, George Em Karniadakis ·

    Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network

    arXiv:2512.13746v5 Announce Type: replace-cross Abstract: Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of thermosets. These heterogeneities generate re…

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