Sensitivity Prewarping for Local Surrogate Modeling
This is an incremental improvement for computational modeling in product design, aimed at reducing costs and improving efficiency by enhancing local surrogate models.
The authors tackled the problem of local surrogate models needing to repeatedly re-learn global trends by proposing a framework that incorporates global sensitivity analysis as a preprocessing step to rotate and rescale inputs, making the model equally sensitive to all directions and validated on benchmark functions and an automotive industry simulator.
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.