The CompModels package for R provides a suite of computer model test functions that can be used for computer model prediction/emulation, uncertainty quantification, and calibration. Moreover, the CompModels package is especially well suited for the sequential optimization of computer models. The package is a mix of real-world physics problems, known mathematical functions, and black-box functions that have been converted into computer models with the goal of Bayesian (i.e., sequential) optimization in mind. Likewise, the package contains computer models that represent either the constrained or unconstrained optimization case, each with varying levels of difficulty. In this paper, we illustrate the use of the package with both real-world examples and black-box functions by solving constrained optimization problems via Bayesian optimization. Ultimately, the package is shown to provide users with a source of computer model test functions that are reproducible, shareable, and that can be used for benchmarking of novel optimization methods.
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For attribution, please cite this work as
Pourmohamad, "The R Journal: CompModels: A Suite of Computer Model Test Functions for Bayesian Optimization", The R Journal, 2021
BibTeX citation
@article{RJ-2021-076,
author = {Pourmohamad, Tony},
title = {The R Journal: CompModels: A Suite of Computer Model Test Functions for Bayesian Optimization},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-076},
doi = {10.32614/RJ-2021-076},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {441-449}
}