Efficient Surrogate Model Development: Optimum Model Form Based on Input Function Characteristics

摘要

Surrogate models statistically relate input data to output data, which are collected by running the complicated system simulation. This study focuses on the identification of the proper surrogate-model form using computational experiments. Eight different surrogate-modeling approaches are evaluated using thirty-five challenge functions with various shapes and numbers of inputs. Data sets for training the surrogate models are generated using Latin Hypercube, Sobol, and Halton sampling methods. In general, ANN, ALAMO and ELM provided the lowest root mean square error and maximum absolute errors for all functions tested.