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

• 成果类型: Article
• 署名作者: Mohammadi, Samira; Cremaschi, Selen
• 署名单位: Auburn University System; Auburn University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.compchemeng.2023.108315
• 发表日期: 2023
• 关键词: Surrogate model; stochastic simulation; Machine Learning; High-fidelity simulation; Intrinsic uncertainty; Uncertainty propagations

摘要

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 Was-serstein 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.