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Bayesian calibration of computer models

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Cited by:

  1. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
  2. Matieyendou Lamboni, 2024. "Optimal Estimators of Cross-Partial Derivatives and Surrogates of Functions," Stats, MDPI, vol. 7(3), pages 1-22, July.
  3. Vanslette, Kevin & Tohme, Tony & Youcef-Toumi, Kamal, 2020. "A general model validation and testing tool," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  4. Leatherman, Erin R. & Dean, Angela M. & Santner, Thomas J., 2017. "Designing combined physical and computer experiments to maximize prediction accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 346-362.
  5. Martin, Niall P.D. & Bishop, Justin D.K. & Choudhary, Ruchi & Boies, Adam M., 2015. "Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis," Applied Energy, Elsevier, vol. 157(C), pages 929-939.
  6. Barde, Sylvain, 2024. "Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
  7. Radu Herbei & L. Mark Berliner, 2014. "Estimating Ocean Circulation: An MCMC Approach With Approximated Likelihoods via the Bernoulli Factory," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 944-954, September.
  8. Giri Gopalan & Christopher K. Wikle, 2022. "A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 22-45, March.
  9. Perrin, G., 2020. "Adaptive calibration of a computer code with time-series output," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
  10. Huaimin Diao & Yan Wang & Dianpeng Wang, 2022. "A D-Optimal Sequential Calibration Design for Computer Models," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
  11. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
  12. Hidy, D. & Barcza, Z. & Haszpra, L. & Churkina, G. & Pintér, K. & Nagy, Z., 2012. "Development of the Biome-BGC model for simulation of managed herbaceous ecosystems," Ecological Modelling, Elsevier, vol. 226(C), pages 99-119.
  13. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
  14. Solai, Elie & Guadagnini, Maxime & Beaugendre, Héloïse & Daccord, Rémi & Congedo, Pietro, 2022. "Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack," Energy, Elsevier, vol. 249(C).
  15. Montserrat Fuentes & Peter Guttorp & Peter Challenor, 2003. "Statistical Assessment of Numerical Models," International Statistical Review, International Statistical Institute, vol. 71(2), pages 201-221, August.
  16. Park, Inseok & Amarchinta, Hemanth K. & Grandhi, Ramana V., 2010. "A Bayesian approach for quantification of model uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 777-785.
  17. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
  18. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
  19. Gross, Eitan, 2015. "Effect of environmental stress on regulation of gene expression in the yeast," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 224-235.
  20. Dario Domingo & Mohammad Royapoor & Hailiang Du & Aaron Boranian & Sara Walker & Michael Goldstein, 2024. "Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model," Energies, MDPI, vol. 17(16), pages 1-28, August.
  21. Paulo, Rui & García-Donato, Gonzalo & Palomo, Jesús, 2012. "Calibration of computer models with multivariate output," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3959-3974.
  22. Storlie, Curtis B. & Reich, Brian J. & Helton, Jon C. & Swiler, Laura P. & Sallaberry, Cedric J., 2013. "Analysis of computationally demanding models with continuous and categorical inputs," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 30-41.
  23. Yawen Guan & Christian Sampson & J. Derek Tucker & Won Chang & Anirban Mondal & Murali Haran & Deborah Sulsky, 2019. "Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 444-463, September.
  24. Trucano, T.G. & Swiler, L.P. & Igusa, T. & Oberkampf, W.L. & Pilch, M., 2006. "Calibration, validation, and sensitivity analysis: What's what," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1331-1357.
  25. Marc Kennedy & Clive Anderson & Anthony O'Hagan & Mark Lomas & Ian Woodward & John Paul Gosling & Andreas Heinemeyer, 2008. "Quantifying uncertainty in the biospheric carbon flux for England and Wales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 109-135, January.
  26. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
  27. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
  28. Jung, Yongsu & Lee, Ikjin, 2021. "Optimal design of experiments for optimization-based model calibration using Fisher information matrix," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  29. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
  30. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  31. Matthew Plumlee, 2014. "Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1581-1591, December.
  32. Hwang, Youngdeok & Kim, Hang J. & Chang, Won & Yeo, Kyongmin & Kim, Yongku, 2019. "Bayesian pollution source identification via an inverse physics model," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 76-92.
  33. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
  34. Choi, Wonjun & Menberg, Kathrin & Kikumoto, Hideki & Heo, Yeonsook & Choudhary, Ruchi & Ooka, Ryozo, 2018. "Bayesian inference of structural error in inverse models of thermal response tests," Applied Energy, Elsevier, vol. 228(C), pages 1473-1485.
  35. Shuaiqiang Liu & Anastasia Borovykh & Lech A. Grzelak & Cornelis W. Oosterlee, 2019. "A neural network-based framework for financial model calibration," Papers 1904.10523, arXiv.org.
  36. Kapusuzoglu, Berkcan & Mahadevan, Sankaran, 2021. "Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
  37. Andrianakis, Ioannis & Challenor, Peter G., 2012. "The effect of the nugget on Gaussian process emulators of computer models," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4215-4228.
  38. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
  39. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
  40. Guillaume Perrin & Christian Soize, 2020. "Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework," Computational Statistics, Springer, vol. 35(1), pages 111-133, March.
  41. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
  42. McDonald, Cory P. & Urban, Noel R., 2010. "Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models," Ecological Modelling, Elsevier, vol. 221(3), pages 428-432.
  43. Aleksandrina Goeva & Henry Lam & Huajie Qian & Bo Zhang, 2019. "Optimization-Based Calibration of Simulation Input Models," Operations Research, INFORMS, vol. 67(5), pages 1362-1382, September.
  44. Kim, Wongon & Yoon, Heonjun & Lee, Guesuk & Kim, Taejin & Youn, Byeng D., 2020. "A new calibration metric that considers statistical correlation: Marginal Probability and Correlation Residuals," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  45. Sun, Yang & Fang, Xiangzhong, 2024. "Efficient calibration of computer models with multivariate output," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
  46. Arnst, M. & Goyal, K., 2017. "Sensitivity analysis of parametric uncertainties and modeling errors in computational-mechanics models by using a generalized probabilistic modeling approach," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 394-405.
  47. Bruno Sansó & Chris Forest, 2009. "Statistical calibration of climate system properties," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 485-503, September.
  48. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
  49. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  50. Chaitanya Kaligotla & Jonathan Ozik & Nicholson Collier & Charles M. Macal & Kelly Boyd & Jennifer Makelarski & Elbert S. Huang & Stacy T. Lindau, 2020. "Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-1.
  51. Sounak Chakraborty & Tanujit Dey & Lingwei Xiang & Joel T. Adler, 2024. "A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States," Stats, MDPI, vol. 7(2), pages 1-13, June.
  52. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
  53. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
  54. Arhonditsis, George B. & Qian, Song S. & Stow, Craig A. & Lamon, E. Conrad & Reckhow, Kenneth H., 2007. "Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake," Ecological Modelling, Elsevier, vol. 208(2), pages 215-229.
  55. K. Sham Bhat & David S. Mebane & Priyadarshi Mahapatra & Curtis B. Storlie, 2017. "Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1453-1467, October.
  56. Zhao, Lufeng & Lu, Zhenzhou & Yun, Wanying & Wang, Wenjin, 2017. "Validation metric based on Mahalanobis distance for models with multiple correlated responses," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 80-89.
  57. Radaideh, Majdi I. & Borowiec, Katarzyna & Kozlowski, Tomasz, 2019. "Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under Bayesian statistics," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 357-377.
  58. Gholami, M. & Torreggiani, D. & Tassinari, P. & Barbaresi, A., 2021. "Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
  59. Theresa Reiker & Monica Golumbeanu & Andrew Shattock & Lydia Burgert & Thomas A. Smith & Sarah Filippi & Ewan Cameron & Melissa A. Penny, 2021. "Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  60. McClarren, Ryan G. & Ryu, D. & Paul Drake, R. & Grosskopf, Michael & Bingham, Derek & Chou, Chuan-Chih & Fryxell, Bruce & van der Holst, Bart & Paul Holloway, James & Kuranz, Carolyn C. & Mallick, Ban, 2011. "A physics informed emulator for laser-driven radiating shock simulations," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1194-1207.
  61. Reichert, P. & White, G. & Bayarri, M.J. & Pitman, E.B., 2011. "Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1638-1655, April.
  62. Li, Luyi & Lu, Zhenzhou, 2018. "A new method for model validation with multivariate output," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 579-592.
  63. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  64. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  65. Simon French, 2003. "Modelling, making inferences and making decisions: The roles of sensitivity analysis," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 11(2), pages 229-251, December.
  66. Li, Wei & Chen, Wei & Jiang, Zhen & Lu, Zhenzhou & Liu, Yu, 2014. "New validation metrics for models with multiple correlated responses," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 1-11.
  67. Mullins, Joshua & Mahadevan, Sankaran, 2014. "Variable-fidelity model selection for stochastic simulation," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 40-52.
  68. Robert MacCallum & Anthony O’Hagan, 2015. "Advances in Modeling Model Discrepancy: Comment on Wu and Browne (2015)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 601-607, September.
  69. Mullins, Joshua & Ling, You & Mahadevan, Sankaran & Sun, Lin & Strachan, Alejandro, 2016. "Separation of aleatory and epistemic uncertainty in probabilistic model validation," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 49-59.
  70. Yuan, Jun & Nian, Victor & He, Junliang & Yan, Wei, 2019. "Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources," Energy, Elsevier, vol. 189(C).
  71. Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
  72. Brendan Kochunas & Xun Huan, 2021. "Digital Twin Concepts with Uncertainty for Nuclear Power Applications," Energies, MDPI, vol. 14(14), pages 1-32, July.
  73. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
  74. Joel A Paulson & Marc Martin-Casas & Ali Mesbah, 2019. "Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-35, August.
  75. Edward Boone & Jan Hannig & Ryad Ghanam & Sujit Ghosh & Fabrizio Ruggeri & Serge Prudhomme, 2022. "Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study," Stats, MDPI, vol. 5(4), pages 1-17, November.
  76. Radaideh, Majdi I. & Kozlowski, Tomasz, 2020. "Surrogate modeling of advanced computer simulations using deep Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  77. Jakub Bijak & Eric Silverman, 2013. "Probability and Social Science. Methodological Relationships between the Two Approaches," Population Studies, Taylor & Francis Journals, vol. 67(1), pages 127-129, March.
  78. Wu, Danqing & Lu, Zhenzhou & Wang, Yanping & Cheng, Lei, 2015. "Model validation and calibration based on component functions of model output," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 59-70.
  79. Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
  80. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
  81. Jonathan Rougier & Martin Kern, 2010. "Predicting snow velocity in large chute flows under different environmental conditions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 737-760, November.
  82. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
  83. Wilkinson Richard David, 2013. "Approximate Bayesian computation (ABC) gives exact results under the assumption of model error," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(2), pages 129-141, May.
  84. Yan Li & Kristofer G. Reyes & Jorge Vazquez-Anderson & Yingfei Wang & Lydia M. Contreras & Warren B. Powell, 2018. "A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 750-767, November.
  85. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
  86. Shi, Lei & Lin, Shih-Po, 2016. "A new RBDO method using adaptive response surface and first-order score function for crashworthiness design," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 125-133.
  87. Suzuki, Kenya & Wada, Ryota & Konno, Yoshihiro & Hiekata, Kazuo & Nanjo, Takashi & Nagakubo, Sadao, 2024. "Impact of epistemic uncertainty on tradeoff in model-based decision support for methane hydrate development system design," Applied Energy, Elsevier, vol. 356(C).
  88. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
  89. Lorenzo Tomassini & Peter Reichert & Hans R. Künsch & Christoph Buser & Reto Knutti & Mark E. Borsuk, 2009. "A smoothing algorithm for estimating stochastic, continuous time model parameters and its application to a simple climate model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 679-704, December.
  90. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
  91. Pasanisi, Alberto & Keller, Merlin & Parent, Eric, 2012. "Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 93-101.
  92. Zhang, Xueping & Han, Zongwei & Ji, Qiang & Zhang, Hongzhi & Li, Xiuming, 2021. "Thermal response tests for the identification of soil thermal parameters: A review," Renewable Energy, Elsevier, vol. 173(C), pages 1123-1135.
  93. Paromita Nath & Sankaran Mahadevan, 2023. "Probabilistic predictive control of porosity in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1085-1103, March.
  94. White, Staci A. & Herbei, Radu, 2015. "A Monte Carlo approach to quantifying model error in Bayesian parameter estimation," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 168-181.
  95. 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).
  96. Wang, Chong & Matthies, Hermann G., 2019. "Novel model calibration method via non-probabilistic interval characterization and Bayesian theory," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 84-92.
  97. Mevin Hooten & Christopher Wikle & Michael Schwob, 2020. "Statistical Implementations of Agent‐Based Demographic Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 441-461, August.
  98. Heeyoung Kim & Justin T. Vastola & Sungil Kim & Jye-Chyi Lu & Martha A. Grover, 2017. "Incorporation of engineering knowledge into the modeling process: a local approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5865-5880, October.
  99. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
  100. Subramanian, Abhinav & Mahadevan, Sankaran, 2023. "Probabilistic physics-informed machine learning for dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  101. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
  102. Jung, Yongsu & Jo, Hwisang & Choo, Jeonghwan & Lee, Ikjin, 2022. "Statistical model calibration and design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  103. Yijie Peng & Michael C. Fu & Bernd Heidergott & Henry Lam, 2020. "Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling," Operations Research, INFORMS, vol. 68(6), pages 1896-1912, November.
  104. Perrin, G., 2016. "Active learning surrogate models for the conception of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 130-136.
  105. Jeong, Cheoljoon & Byon, Eunshin, 2024. "Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method," Applied Energy, Elsevier, vol. 360(C).
  106. Jill Johnston & Jacqueline MacDonald Gibson, 2015. "Indoor Air Contamination from Hazardous Waste Sites: Improving the Evidence Base for Decision-Making," IJERPH, MDPI, vol. 12(12), pages 1-18, November.
  107. Zhao, Zilong & Lin, Yu-Feng & Stumpf, Andrew & Wang, Xinlei, 2022. "Assessing impacts of groundwater on geothermal heat exchangers: A review of methodology and modeling," Renewable Energy, Elsevier, vol. 190(C), pages 121-147.
  108. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
  109. Antony M. Overstall & David C. Woods, 2013. "A Strategy for Bayesian Inference for Computationally Expensive Models with Application to the Estimation of Stem Cell Properties," Biometrics, The International Biometric Society, vol. 69(2), pages 458-468, June.
  110. Frigg, Roman & Smith, Leonard A. & Stainforth, David A., 2015. "An assessment of the foundational assumptions inhigh-resolution climate projections: the case of UKCP09," LSE Research Online Documents on Economics 61635, London School of Economics and Political Science, LSE Library.
  111. Park, Inseok & Grandhi, Ramana V., 2014. "A Bayesian statistical method for quantifying model form uncertainty and two model combination methods," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 46-56.
  112. Kennedy, Marc C. & Anderson, Clive W. & Conti, Stefano & O’Hagan, Anthony, 2006. "Case studies in Gaussian process modelling of computer codes," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1301-1309.
  113. Giri Gopalan & Birgir Hrafnkelsson & Christopher K. Wikle & Håvard Rue & Guðfinna Aðalgeirsdóttir & Alexander H. Jarosch & Finnur Pálsson, 2019. "A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 669-692, December.
  114. Villez, Kris & Del Giudice, Dario & Neumann, Marc B. & Rieckermann, Jörg, 2020. "Accounting for erroneous model structures in biokinetic process models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  115. Hjelkrem, Anne-Grete Roer & Höglind, Mats & van Oijen, Marcel & Schellberg, Jürgen & Gaiser, Thomas & Ewert, Frank, 2017. "Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments," Ecological Modelling, Elsevier, vol. 359(C), pages 80-91.
  116. Merle, X. & Cinnella, P., 2015. "Bayesian quantification of thermodynamic uncertainties in dense gas flows," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 305-323.
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