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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21995 |
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| _version_ | 1866912509031612416 |
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| author | Li, Yezhuo Zhang, Qiong Limaye, Madhura Li, Gang |
| author_facet | Li, Yezhuo Zhang, Qiong Limaye, Madhura Li, Gang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization Li, Yezhuo Zhang, Qiong Limaye, Madhura Li, Gang Applications 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. |
| title | Uncertainty Estimation of the Optimal Decision with Application to Cure Process Optimization |
| topic | Applications |
| url | https://arxiv.org/abs/2507.21995 |