An Automatically Differentiated Kriging‐Based Framework for Reliability‐Based Design Optimization

Reliability‐Based Design Optimization (RBDO) can require substantial computational effort because of repeated reliability evaluations and sensitivity calculations. This study investigates a Kriging‐assisted RBDO framework incorporating reverse‐mode automatic differentiation (AD). Original performance‐function evaluations provide training data, while AD is applied to the Kriging mean predictors to obtain sensitivities for Performance Measure Approach (PMA)‐based reliability analysis. The original black‐box model is not differentiated. Two analytical benchmark problems and a cantilever‐beam design problem are used to examine the framework. Relative to the classical RBDO implementation considered, the reported total constraint‐evaluation counts decrease by approximately 96% and 73% for the first two benchmark problems, respectively, while the cantilever‐beam example satisfies the prescribed target reliability index. Overall, the results indicate the computational potential of combining Kriging surrogate modeling with AD‐based sensitivity evaluation within PMA‐based RBDO. The study therefore presents an AD–Kriging implementation for surrogate‐based RBDO and demonstrates its application on representative benchmark problems.