Abstract This paper is concerned with the development of metamodels specifically tailored for mixed variables, in particular continuous and categorical variables. Practically, we propose a surrogate model based on multiple kernel regression, and apply it to six benchmark test functions and a rigid frame structural analysis. When compared to other metamodels (support vector regression, ordinary least squares), the numerical results show the efficiency of the method, related to the flexible selection of different types of kernel functions. Further work will include the use of these metamodels for mixed-variable surrogate-based optimization involving computationally expensive simulations. 2
