A Framework for Validation of Computer Models

By Maria J. Bayarri, James O. Berger, Rui Paulo, J. Sacks, John A. Cafeo, James C. Cavendish, Chin-Hsu Lin, Jian Tu. Technometrics, 2007

729 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af9ba46ad2bf713c3c3699c242d59dc9c8d5f4b1

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Citations in the corpus

RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY (2012, methodology): “In such case, the suggested methodology can be used in the context of validation (see [8, 12, 13]).”
Computer model validation with functional output (2007, methodology): “[4], indicating the magnitude of the possible error in prediction.”
The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2019, methodology): “nce CMC u are obtained by Monte Carlo sampling (23) and can be considered as exact. The errors are measured in the Frobenius norm. 8 3. Bayesian inference In this section, we ﬁrst introduce following [11,22,23] the statistical generating model for the true process underlying the observed data. Each ”
Diagnostics for Gaussian Process Emulators (2009, methodology): “Bayarri et al. (2007) presented a six-step process for computer model validation based on Bayesian and likelihood methodology.”
Sequential design of computer experiments for the estimation of a probability of failure (2010, methodology): “This line of research has its roots in the field of design and analysis of computer experiments (see, e.g., Sacks et al., 1989; Currin et al., 1991; Welch et al., 1992; Oakley and O’Hagan, 2002, 2004; Oakley, 2004; Bayarri et al., 2007).”
A Bayesian approach for parameter estimation and prediction using a computationally intensive model (2014, background): “Introduction Bayesian calibration of computer models [1, 2, 3] combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, in order to carry out statistical and scientific inferences.”
Review of multi-fidelity models (2016, neutral): “[20] [50] [66] [79] Calibration + comprehensive correction”
Learning about physical parameters: the importance of model discrepancy (2014, methodology): “Since then, their inferential ideas and modeling framework have been widely adopted and further developed, e.g. Higdon et al (2004, 2008), Qian and Wu (2008), Goldstein and Rougier (2009), Bayarri et al (2007), Gramacy and Lee (2008).”
Key computational modeling issues in Integrated Computational Materials Engineering (2013, methodology): “The Bayesian framework is one of the commonly adopted frameworks for model validation [207].”
Bayesian analysis of computer code outputs: A tutorial (2006, background): “Allowing for uncertainty in smoothness parameters is also possible (see [12]) but increases the computational complexity.”
Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data (2009, methodology): “The methodology from Bayarri et al. (2007b) that we are adopting does not accommodate qualitative inputs such as barrier type.”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “Often, Gaussian-process models are used to attempt to correct model inadequacy [114, 115, 100, 157, 18].”
Computer Model Calibration Using High-Dimensional Output (2008, methodology): “The ﬁrst is the interplay between the discrepancy term δ ( x ) and the calibration parameters θ ; this is noted in the discussion of Kennedy and O’Hagan (2001), as well as in a number of other articles (Hig-don et al. 2004; Loeppky, Bingham, Sacks, and Welch 2005; Bayarri et al. 2007).”