Surrogates, or data-driven models, play an increasingly important role in the evaluation of engineering systems. In recent years, machine learning has offered more types for the 'surrogate family' and boosted the application of surrogates. To build surrogates, one of prerequisites is data, the generation of which can be time-consuming and prohibitively expensive for real-world engineering systems when no preliminary data is available.