Data-Driven Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations

ArXi:2512.11946v2 Announce Type: replace-cross Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input variables influence data-driven models is essential. Partial Dependence Plots (PDPs) are widely used for interpreting black-box models by showing the average effect of an input variable on the prediction. However, their global sensitivity metric can be misleading when strong interactions are present, as averaging tends to obscure interaction effects.