论文标题
Compmodels：一套贝叶斯优化的计算机模型测试功能
CompModels: A suite of computer model test functions for Bayesian optimization
论文作者
论文摘要
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/仿真，不确定性量化和校准，但尤其是计算机模型的顺序优化。该软件包是现实世界中物理问题，已知数学功能和黑框函数的混合，这些功能已转换为计算机模型，并以贝叶斯（即顺序）优化为目标。同样，软件包包含代表约束或不受约束的优化情况的计算机模型，每个案例都有不同的困难水平。在本文中，我们通过通过贝叶斯优化解决了约束优化问题，说明了与现实世界示例和黑框函数一起使用的软件包的使用。最终，该软件包被证明可以为用户提供可重现，可共享的计算机模型测试功能的来源，并且可用于基准进行新颖优化方法的基准测试。
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.
