Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming than an low-fidelity (LF) model. To take advantages of both HF and LF models, multi-fidelity surrogate models that aim to integrate information from both HF and LF models have gained increasing popularity. In this paper, a multi-fidelity surrogate model based on support vector regression named as Co_SVR is developed by combining HF and LF models. In Co_SVR, a kernel function is used to map the map the difference between the HF and LF models. Besides, a heuristic algorithm is used to obtain the optimal parameters of Co_SVR. The proposed Co_SVR is compared with two popular multi-fidelity surrogate models Co_Kriging model, Co_RBF model, and their single-fidelity surrogates through several numerical cases and a pressure vessel design problem. The results show that Co_SVR provides competitive prediction accuracy for numerical cases, and presents a better performance compared with the Co_Kriging and Co_RBF models and single-fidelity surrogate models.
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA（航空航天工程学院，佐治亚理工学院，亚特兰大，GA）；School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA（计算科学与工程学院，佐治亚理工学院，亚特兰大，GA）；Air Force Research Lab, Wright-Patterson AFB, Dayton, Ohio（空军研究实验室，威格伍德-帕特森空军基地，代顿，俄亥俄）
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
School of Computation, Information and Technology, Technische Universität München（慕尼黑工业大学计算、信息与技术学院）；Institute of System Dynamics and Control, German Aerospace Center (DLR)（德国航空航天中心系统动力学与控制研究所）
A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校机械科学与工程系）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions
Stanford University（斯坦福大学）；Centro di Ricerca Matematica Ennio De Giorgi, Scuola Normale Superiore（比萨高等师范学院恩尼奥·德·乔治数学研究中心）；University of Notre Dame（圣母大学）
Ranking over Regression for Bayesian Optimization and Molecule Selection
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；CIFAR Lebovic Fellow（CIFAR 莱博维奇 Fellow）；Canada（加拿大）；Chair, Director of Acceleration Consortium（150 职位，加速联盟主任）
