The R Journal: article published in 2021, volume 13:2

CompModels: A Suite of Computer Model Test Functions for Bayesian Optimization
Tony Pourmohamad , The R Journal (2021) 13:2, pages 441-449.

Abstract 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.

Received: 2021-02-22; online 2021-08-17, supplementary material, (1 KiB)
CRAN packages: CompModels, laGP

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-076,
  author = {Tony Pourmohamad},
  title = {{CompModels: A Suite of Computer Model Test Functions for
          Bayesian Optimization}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-076},
  url = {https://doi.org/10.32614/RJ-2021-076},
  pages = {441--449},
  volume = {13},
  number = {2}
}