AFAICT this is a fancy word for parameter estimation from simulation-heavy communities. Distinct from calibration for prbabilistic predictions.
Closely related to AutoML, in that surrogate optimisation is a popular tool for such, and adaptive design of experiment.
Surrogate optimisation
Classic GP surrogate optimisation is a popular tool for model calibration, see Kennedy and O’Hagan (2001) for a classic example. More recent: Plumlee (2017).
MMD
See Dellaporta et al. (2022) for the application of maximum mean discrepancy to the problem of model calibration.
References
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Bayarri, Maria J, James O Berger, Rui Paulo, Jerry Sacks, John A Cafeo, James Cavendish, Chin-Hsu Lin, and Jian Tu. 2007. “A Framework for Validation of Computer Models.” Technometrics 49 (2): 138–54.
Cockayne, Jon, and Andrew B. Duncan. 2020. “Probabilistic Gradients for Fast Calibration of Differential Equation Models,” September.
Dellaporta, Charita, Jeremias Knoblauch, Theodoros Damoulas, and François-Xavier Briol. 2022. “Robust Bayesian Inference for Simulator-Based Models via the MMD Posterior Bootstrap.” arXiv:2202.04744 [Cs, Stat], February.
Doherty, John. 2015. Calibration and uncertainty analysis for complex environmental models.
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Kennedy, Marc C., and Anthony O’Hagan. 2001. “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63 (3): 425–64.
Koermer, Scott, Justin Loda, Aaron Noble, and Robert B. Gramacy. 2023. “Active Learning for Simulator Calibration.” arXiv.
Laloy, Eric, and Diederik Jacques. 2019. “Emulation of CPU-Demanding Reactive Transport Models: A Comparison of Gaussian Processes, Polynomial Chaos Expansion, and Deep Neural Networks.” Computational Geosciences 23 (5): 1193–1215.
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O’Hagan, A. 1978. “Curve Fitting and Optimal Design for Prediction.” Journal of the Royal Statistical Society: Series B (Methodological) 40 (1): 1–24.
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Pleiss, Geoff, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger. 2017. “On Fairness and Calibration.” In Advances In Neural Information Processing Systems.
Plumlee, Matthew. 2017. “Bayesian Calibration of Inexact Computer Models.” Journal of the American Statistical Association 112 (519): 1274–85.
Regis, Rommel G., and Christine A. Shoemaker. 2013. “Combining Radial Basis Function Surrogates and Dynamic Coordinate Search in High-Dimensional Expensive Black-Box Optimization.” Engineering Optimization 45 (5): 529–55.
Sacks, Jerome, Susannah B. Schiller, and William J. Welch. 1989. “Designs for Computer Experiments.” Technometrics 31 (1): 41–47.
Sacks, Jerome, William J. Welch, Toby J. Mitchell, and Henry P. Wynn. 1989. “Design and Analysis of Computer Experiments.” Statistical Science 4 (4): 409–23.
Thiagarajan, Jayaraman J., Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, and Brian Spears. 2020. “Designing Accurate Emulators for Scientific Processes Using Calibration-Driven Deep Models.” Nature Communications 11 (1): 5622.
Tonkin, Matthew, and John Doherty. 2009. “Calibration-Constrained Monte Carlo Analysis of Highly Parameterized Models Using Subspace Techniques.” Water Resources Research 45 (12).
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