“…Improving computational efficiency of simulation-based design procedures has been targeted by numerous research endeavours. These efforts focused on the development of strictly algorithmic approaches, both intrusive (e.g., gradient-based procedures accelerated by means of adjoint sensitivities 27 , 28 ), and non-intrusive (e.g., trust-region methods with sparse sensitivity updates 29 , 30 , as well as surrogate-based frameworks involving data-driven 7 , and physics-based metamodels 5 . Although approximation surrogates (kriging 31 , radial-basis functions 32 , support vector regression 33 , polynomial chaos expansion 34 , 35 , neural networks 36 , Gaussian process regression 37 , polynomial regression 38 ) are by far more popular, their application is limited by the curse of dimensionality, which is particularly troublesome when handling nonlinear outputs of high-frequency structures.…”