An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity models. Issue 3 (August 2016)

• Record Type: Journal Article
• Authors: Zhou, Qi; Shao, Xinyu; Jiang, Ping; Gao, Zhongmei; Wang, Chaochao; Shu, Leshi
• Is Part Of: Advanced engineering informatics. Volume 30:Issue 3(2016:Aug.)
• Page Start: 283
• Page End: 297
• Publication Date: 2016-08
• Subjects: Variable fidelity metamodel -- Already-acquired information -- Active learning strategy -- Metamodel-based design optimization -- Kriging; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2016.04.004
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: 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. Abstract: 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.