In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input-output relationships. However, narrowing focus to local scale means that global trends must be re-learned over and over again. In this article, we propose a framework for incorporating information from a global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the "warped simulator" is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.
翻译:在不断努力提高产品质量和降低运营成本的过程中,正在越来越多地采用计算模型来确定产品设计或配置的可行性。通过本地模型取代这些计算机实验的模型,这种实验只考虑到短距离互动而引起宽度,能够解决对复杂的投入-产出关系的巨大分析。然而,将重点缩小到局部范围意味着全球趋势必须反复地重新了解。在本条中,我们提出了一个框架,将全球敏感性分析中的信息作为输入旋转和调整前处理步骤纳入替代模型中。我们讨论了基于内核回归的若干敏感分析方法之间的关系,然后说明它们如何导致输入变量的转换。具体地说,我们执行一种投入扭曲,即“扭曲模拟器”对所有输入方向都同样敏感,释放当地模型以关注当地动态。关于观测数据和基准测试功能的数值实验,包括汽车业的高维计算机模拟器,提供经验验证。