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arXiv · 2507.21995

Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization

Yezhuo Li·Qiong Zhang·Madhura Limaye·Gang Li
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Abstract

Decision-making in manufacturing often involves optimizing key process parameters using data collected from simulation experiments. Gaussian processes are widely used to surrogate the underlying system and guide optimization. Uncertainty often inherent in the decisions given by the surrogate model due to limited data and model assumptions. This paper proposes a surrogate model-based framework for estimating the uncertainty of optimal decisions and analyzing its sensitivity with respect to the objective function. The proposed approach is applied to the composite cure process simulation in manufacturing.

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Yezhuo Li, Qiong Zhang, Madhura Limaye, Gang Li. 2025-07-29. Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization. https://arxiv.org/abs/2507.21995

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