Combining Field Data and Computer Simulations for Calibration and Prediction

By D. Higdon, Marc C. Kennedy, James C. Cavendish, John A. Cafeo, R. Ryne. SIAM Journal on Scientific Computing, 2005

665 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e5a00bb5abea3d947b5196be4c81721ad0e52e80

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Citations in the corpus

Occupancy data at different spatial resolutions: Building energy performance and model calibration (2021, methodology): “Since the iterative calibration process can be computationally inensive, a Gaussian process (GP) emulator or metamodel is used to mulate the energy model and combine measured data with simulation ata [46].”
Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments (2008, methodology): “Following Higdon et al. (2004), a fully Bayesian approach can be developed that computes the posterior of θ3 for the prediction. One possibility is to modify the conditional sampling method in (14) by including a step to sample from p(θ3|yl,yh, θ3) given in (10). Because this function has a complica”
Performance study of multi-fidelity gradient enhanced kriging (2015, methodology): “For example, various multi-fidelity surrogate modelling methods are already presented in the literature (Craig et al. 1998; Cumming and Goldstein 2009; Goldstein and Wooff 2007; Higdon et al. 2004; Kennedy and O’Hagan 2000; Forrester et al. 2007; Bandler et al. 1994).”
Choosing the Sample Size of a Computer Experiment: A Practical Guide (2009, methodology): “A theoretical analysis by Chen (1996) showed that for d = 1, the order of the maximum mean squared prediction error is n−n for very smooth output functions and equally spaced designs.”
Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data (2009, methodology): “The key components of the approach outlined here are the use of Gaussian process response-surface approximations to a computer model, following on work in Sacks et al. (1989), Currin et al.”
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].”
Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012, methodology): “Previous work [8, 17, 18, 25] has combined the two approaches above into one model updating formulation represented by”
A Framework for Validation of Computer Models (2007, methodology): “The spotweld example was examined from a fully Bayesian perspective in Higdon et al. (2004).”
The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2019, background): “Bayesian inference In this section, we first introduce following [11, 22, 23] the statistical generating model for the true process underlying the observed data.”