Surrogate modeling of advanced computer simulations using deep Gaussian processes
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DOI: 10.1016/j.ress.2019.106731
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Cited by:
- Yuan, Xiukai & Qian, Yugeng & Chen, Jingqiang & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2023. "Global failure probability function estimation based on an adaptive strategy and combination algorithm," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
- Xu, Yanwen & Renteria, Anabel & Wang, Pingfeng, 2022. "Adaptive surrogate models with partially observed information," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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Keywords
Deep GP; Bayesian learning; Gaussian processes; Uncertainty quantification; Nuclear simulations; Nuclear reactor safety;All these keywords.
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