“…Some of the mentioned methods can be named as; adjoints sensitivities into gradient-based optimization algorithms, 11 accelerating local procedures via sparse sensitivity updates, 12,13 the employment of machine learning methods, 14 as well as surrogate-assisted procedures involving both data-driven, [15][16][17][18] physics-based surrogates, 19 kriging, 20,21 radial basis functions (RBF), 16 Gaussian process regression (GPR), 22 neural networks, [23][24][25][26][27] support vector regression, [28][29][30] polynomial response surfaces, 31 or fuzzy models. 32 Ensemble learning is the mechanism by which many models (often called "weak learners") are strategically created and combined to solve a specific computer intelligence challenge and primarily employed to boost the efficiency of a model (classification, prediction, approximation of functions, etc.) or to lower the risk of a weak learner collection.…”