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Technometrics Volume 55, 2013 - Issue 4
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Original Articles

Computer Model Calibration Using the Ensemble Kalman Filter

Dave Higdon Statistical Sciences, Group Los Alamos National Laboratory, Los Alamos, NM, 87545
,
Jim Gattiker Statistical Sciences, Group Los Alamos National Laboratory, Los Alamos, NM, 87545
,
Earl Lawrence Statistical Sciences, Group Los Alamos National Laboratory, Los Alamos, NM, 87545
,
Charles Jackson Institute for Geophysics University of Texas, Austin, TX, 78759
,
Michael Tobis Institute for Geophysics University of Texas, Austin, TX, 78759
,
Matt Pratola Department of Statistics, The Ohio State University, Columbus, OH, 43210
,
Salman Habib High Energy Physics Division Argonne National Laboratory, Argonne, IL, 60439
,
Katrin Heitmann High Energy Physics Division Argonne National Laboratory, Argonne, IL, 60439
&
Steve Price Fluid Dynamics Group Los Alamos National Laboratory, Los Alamos, NM, 87545
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Pages 488-500 | Received 01 Apr 2012, Published online: 22 Nov 2013

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Rui Tuo, Shiyuan He, Arash Pourhabib, Yu Ding & Jianhua Z. Huang. (2023) A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models. Journal of the American Statistical Association 118:542, pages 883-897.
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Yang Sun & Xiangzhong Fang. (2022) Sparse calibration based on adaptive lasso penalty for computer models. Communications in Statistics - Simulation and Computation 0:0, pages 1-15.
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Jaesung Lee, Chao Wang, Xiaoyu Sui, Shiyu Zhou & Junhong Chen. (2022) Landmark-embedded Gaussian process with applications for functional data modeling. IISE Transactions 54:11, pages 1033-1046.
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Chih-Li Sung, Ying Hung, William Rittase, Cheng Zhu & C. F. J. Wu. (2020) Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study. Journal of the American Statistical Association 115:532, pages 1664-1674.
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Matthias HY Tan. (2018) Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations. Technometrics 60:2, pages 209-221.
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Drew Yarger, Benjamin Moore Wagman, Kenny Chowdhary & Lyndsay Shand. (2024) Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields. Journal of Advances in Modeling Earth Systems 16:4.
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Chih‐Li Sung & Rui Tuo. (2024) A review on computer model calibration. WIREs Computational Statistics 16:1.
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SungKu Kang, Ran Jin, Xinwei Deng & Ron S. Kenett. (2021) Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective. Journal of Intelligent Manufacturing 34:2, pages 415-428.
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Ron S. Kenett, Shelemyahu Zacks & Peter GedeckRon S. Kenett, Shelemyahu Zacks & Peter Gedeck. 2023. Industrial Statistics. Industrial Statistics 287 317 .
Benedict Götz, Sebastian Kersting & Michael Kohler. (2020) Estimation of an improved surrogate model in uncertainty quantification by neural networks. Annals of the Institute of Statistical Mathematics 73:2, pages 249-281.
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Michael Kohler & Adam Krzyżak. (2021) Estimation of a density using an improved surrogate model. Electronic Journal of Statistics 15:1.
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Georgios Karagiannis, Bledar A. Konomi & Guang Lin. (2019) On the Bayesian calibration of expensive computer models with input dependent parameters. Spatial Statistics 34, pages 100258.
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Oksana A. Chkrebtii & David A. Campbell. (2019) Adaptive step-size selection for state-space probabilistic differential equation solvers. Statistics and Computing 29:6, pages 1285-1295.
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Srinivasan Venkatramanan, Jiangzhuo Chen, Arindam Fadikar, Sandeep Gupta, Dave Higdon, Bryan Lewis, Madhav Marathe, Henning Mortveit & Anil Vullikanti. (2019) Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints. PLOS Computational Biology 15:9, pages e1007111.
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Michael Kohler & Adam Krzyzak. (2019) Estimation of a Density From an Imperfect Simulation Model. IEEE Transactions on Information Theory 65:3, pages 1535-1546.
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Ayan Biswas, Kelly R. Moran, Earl Lawrence & James Ahrens. (2018) Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators. Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators.
Michael Kohler, Adam Krzyżak, Shashidhar Mallapur & Roland Platz. (2018) Uncertainty Quantification in Case of Imperfect Models: A Non‐Bayesian Approach. Scandinavian Journal of Statistics 45:3, pages 729-752.
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Georgios Karagiannis & Guang Lin. (2017) On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. Journal of Computational Physics 342, pages 139-160.
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Razi Sheikholeslami & Saman Razavi. (2017) Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models. Environmental Modelling & Software 93, pages 109-126.
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Rui Tuo & C. F. Jeff Wu. (2015) Efficient calibration for imperfect computer models. The Annals of Statistics 43:6.
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