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Nuclear Science and Engineering Volume 193, 2019 - Issue 1-2: Selected papers from the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics September 3–8, 2017, Xi’an, China
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Technical Papers

Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark

Chen WangUniversity of Illinois at Urbana-Champaign, Department of Nuclear, Plasma and Radiological Engineering, 223 Talbot Laboratory, 104 South Wright Street, Urbana, Illinois61801Correspondence[email protected]
ORCID Iconhttp://orcid.org/0000-0003-4511-168X
ORCID Icon,
Xu WuMassachusetts Institute of Technology, Department of Nuclear Science and Engineering, Cambridge, Massachusetts
&
Tomasz KozlowskiUniversity of Illinois at Urbana-Champaign, Department of Nuclear, Plasma and Radiological Engineering, 223 Talbot Laboratory, 104 South Wright Street, Urbana, Illinois61801
Pages 100-114 | Received 28 Mar 2018, Accepted 07 Jul 2018, Published online: 10 Aug 2018

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Helin Gong, Sibo Cheng, Zhang Chen & Qing Li. (2022) Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics. Nuclear Science and Engineering 196:6, pages 668-693.
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Articles from other publishers (12)

Mandana Soleimani, Mohsen Esmaeilbeigi, Roberto Cavoretto & Alessandra De Rossi. (2024) Analyzing the effects of various isotropic and anisotropic kernels on critical heat flux prediction using Gaussian process regression. Engineering Applications of Artificial Intelligence 133, pages 108351.
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Ziyu Xie, Mahmoud Yaseen & Xu Wu. (2024) Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data. Computer Methods in Applied Mechanics and Engineering 420, pages 116721.
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Mooyoung Yoo. (2023) Development of a Simulator for Household Refrigerator Using Equation-Based Optimization Control with Bayesian Calibration. Machines 12:1, pages 12.
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Chen Wang, Xu Wu, Ziyu Xie & Tomasz Kozlowski. (2023) Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference. Energies 16:22, pages 7664.
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Haoding Xu, Xuzhen He, Feng Shan, Gang Niu & Daichao Sheng. (2023) Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review. Modelling 4:4, pages 426-453.
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Stefano Riva, Carolina Introini, Stefano Lorenzi & Antonio Cammi. (2023) Hybrid data assimilation methods, Part I: Numerical comparison between GEIM and PBDW. Annals of Nuclear Energy 190, pages 109864.
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Riccardo Cocci, Guillaume Damblin, Alberto Ghione, Lucia Sargentini & Didier Lucor. (2022) Extension of the CIRCE methodology to improve the Inverse Uncertainty Quantification of several combined thermal-hydraulic models. Nuclear Engineering and Design 398, pages 111974.
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Giovanni Roma, Federico Antonello, Francesco Di Maio, Nicola Pedroni, Enrico Zio, Andrea Bersano, Cristina Bertani & Fulvio Mascari. (2022) Passive safety systems analysis: A novel approach for inverse uncertainty quantification based on Stacked Sparse Autoencoders and Kriging metamodeling. Progress in Nuclear Energy 148, pages 104209.
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Xu Wu, Ziyu Xie, Farah Alsafadi & Tomasz Kozlowski. (2021) A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes. Nuclear Engineering and Design 384, pages 111460.
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Yang Liu, Dewei Wang, Xiaodong Sun, Yang Liu, Nam Dinh & Rui Hu. (2021) Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments. Reliability Engineering & System Safety 212, pages 107636.
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B.T. Jiang, J. Zhou, X.B. Huang & P.F. Wang. (2020) Prediction of critical heat flux using Gaussian process regression and ant colony optimization. Annals of Nuclear Energy 149, pages 107765.
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Riccardo Cocci, Guillaume Damblin, Alberto Ghione, Lucia Sargentini & Didier Lucor. (2022) Extension of the Circe Methodology to Improve the Inverse Uncertainty Quantification of Several Combined Thermal-Hydraulic Models. SSRN Electronic Journal.
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