An active learning metamodeling approach by sequentially exploiting difference information from variable fidelity models
We propose an active learning variable fidelity metamodeling approach (AL VFM).Information from high fidelity and low fidelity models is integrated in AL VFM.An active learning strategy is introduced to use the already acquired information.Numerical and engineering cases verify the applicability of the proposed approach. Complex system engineering design optimization based on simulation is a very time consuming, even computationally prohibitive process. To relieve the computational burden, metamodels are commonly used to replace the computation intensive simulations. In this paper, an active learning variable fidelity (VF) metamodeling approach (AL VFM) is proposed for the purpose of integrating information from both low fidelity (LF) and high fidelity (HF) models. In AL VFM, Kriging metamodel is adopted to map the difference between the HF and LF models aiming to approach the HF model on the entire domain. Besides, a general active learning strategy is introduced in AL VFM to make full use of the already acquired information to guide the VF metamodeling. The already acquired information represents the location of regions where the differences between the HF and LF models are multi model, non smooth and have abrupt changes. Several numerical and engineering cases with different degrees of difficulty verify the applicability of the proposed VF metamodeling approach.
Adv. Eng. Informatics