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, but in particular, 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.

翻译：R 的 CompModels 软件包提供了一套计算机模型测试功能, 可用于计算机模型预测/ 模拟、 不确定性量化和校准, 特别是计算机模型的顺序优化。 软件包混合了现实世界物理问题、 已知数学函数和黑盒函数, 转化成计算机模型, 目的是让 Bayesian ( 顺序) 优化。 同样, 软件包包含计算机模型, 既代表受限制的或不受限制的优化案例, 每一个都有不同程度的困难。 在本文中, 我们用真实世界的例子和黑盒函数来说明软件包的使用情况, 通过 Bayesian 优化解决受限制的优化问题。 最终, 软件包向用户提供了计算机模型测试功能的来源, 这些功能可以复制、 共享, 并且可以用于新优化方法的基准。