CMS: a novel surrogate model with hierarchical structure based on correlation mapping

As an effective approximation tool, surrogate models have been extensively studied and play an increasingly important role in different areas of engineering. In this paper, a novel surrogate model, termed correlation mapping surrogate (CMS), is proposed based on the Rayleigh quotient and the multi-fidelity surrogate framework. The CMS model has a distinct hierarchical structure because of its step-by-step modeling process, enabling it to obtain accurate predictions relying on a small number of samples alone. To evaluate its prediction accuracy, a series of comparative experiments are conducted, and four popular surrogates, namely Kriging, polynomial response surface, radial basis function, and least-squares support vector regression, are selected as the benchmark models. The key issues of the CMS model, that is, its robustness and ability to handle practical problems, are also investigated. The results demonstrate that the CMS model shows a higher performance on both numerical and practical engineering problems than the other benchmark models, indicating its satisfactory feasibility, practicality, and stability.