A new approach to building surrogate models of high-fidelity stochastic simulations: PARIN (PARameter as Input-variable)

Abstract

High-fidelity simulations are computationally expensive to evaluate for optimization and sensitivity analysis applications. One popular method to avoid this problem is utilizing surrogate models, which approximate the simulations with cheaper-to-evaluate functions. The existing surrogate modeling techniques are designed for deterministic systems, with only a few approaches available for stochastic simulations. This study proposes a new method, called PARIN (PARameter as INput), to efficiently construct accurate surrogate models of high-fidelity stochastic simulations. PARIN is compared to existing approaches in terms of accuracy and efficiency. The results reveal that PARIN generally has a lower normalized root mean square error in predicting the mean and standard deviation of the simulation outputs and that the output distribution predicted by PARIN has the lowest Wasserstein distance from the actual output distribution. However, both metrics for PARIN estimates deteriorate for simulations with a significantly large number of input variables in low computational-budget cases.