Journal ArticleDOI
Follow-Up Experimental Designs for Computer Models and Physical Processes
Pritam Ranjan,Wilson W. Lu,Derek Bingham,Shane Reese,Brian J. Williams,Chuan-Chih Chou,F. W. Doss,Michael Grosskopf,James Paul Holloway +8 more
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This paper develops a methodology for selecting optimal follow-up designs based on integrated mean squared error that help to capture and reduce prediction uncertainty as much as possible.Abstract:
In many branches of physical science, when the complex physical phenomena are either too expensive or too time consuming to observe, deterministic computer codes are often used to simulate these processes. Nonetheless, true physical processes are also observed in some disciplines. It is preferred to integrate both the true physical process and the computer model data for better understanding of the underlying phenomena. In this paper, we develop a methodology for selecting optimal follow-up designs based on integrated mean squared error that help us capture and reduce prediction uncertainty as much as possible. We also compare the efficiency of the optimal designs with the intuitive choices for the follow-up computer and field trials.read more
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A preposterior analysis to predict identifiability in the experimental calibration of computer models
TL;DR: In this paper, a form of preposterior analysis is developed to predict the degree of identifiability that will result after conducting the physical experiments for a given experimental design, in the sense that it is difficult to precisely estimate the parameters and to distinguish between the effects of parameter uncertainty and model discrepancy.
Proceedings Article
Minimax Approach to Variable Fidelity Data Interpolation.
Alexey Zaytsev,Evgeny Burnaev +1 more
TL;DR: This paper obtains minimax interpolation errors for single and variable fidelity scenarios for a multivariate Gaussian process regression and calculates the optimal shares of variable fidelity data samples under the given computational budget constraint.
Journal ArticleDOI
Designing combined physical and computer experiments to maximize prediction accuracy
TL;DR: Comparisons are made among designs that are locally optimal under the minimum integrated mean squared prediction error criterion for the combined physical system and simulator experiments, with a fixed design for the component not being optimized.
Journal ArticleDOI
Robust Experimental Designs for Model Calibration
TL;DR: This paper proposes an optimal design approach that is robust to potential model discrepancies and shows that its designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancy.
Journal ArticleDOI
A Gaussian Process Emulator Based Approach for Bayesian Calibration of a Functional Input
Abstract: Bayesian calibration of a functional input/parameter to a time-consuming simulator based on a Gaussian process (GP) emulator involves two challenges that distinguish it from other parameter calibra...
References
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Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
Journal ArticleDOI
The design and analysis of computer experiments
TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
Journal ArticleDOI
Bayesian Calibration of computer models
Marc C. Kennedy,Anthony O'Hagan +1 more
TL;DR: A Bayesian calibration technique which improves on this traditional approach in two respects and attempts to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best‐fitting parameter values is presented.
Journal ArticleDOI
Minimax and maximin distance designs
TL;DR: In this article, the authors developed the notions of minimax and maximin distance sets (designs) intended for use in the selection-of-sites problem when the underlying surface is modeled by a prior distribution and observations are made without error.