Article
Computer Science, Artificial Intelligence
Lili Zhang, Yuda Wu, Ping Jiang, Seung-Kyum Choi, Qi Zhou
Summary: The NHLF-Co-Kriging method proposed in this work is able to handle multiple non-hierarchical low-fidelity models effectively by optimizing scale factors, providing more accurate MF surrogate models under a limited computational budget.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Lili Zhang, Seung-Kyum Choi, Tingli Xie, Ping Jiang, Jiexiang Hu, Jasuk Koo
Summary: This study constructs Kriging-based multi-fidelity surrogate models to accelerate fatigue analysis of welded joints, taking into consideration different fidelity levels of finite element models for improved accuracy. By using verification methods and uncertainty quantification metrics, the most suitable surrogate models are determined and ranked using the EW-TOPSIS technique.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Multidisciplinary
J. M. Winter, J. W. J. Kaiser, S. Adami, I. S. Akhatov, N. A. Adams
Summary: In this work, a novel framework combining multi-fidelity Gaussian Process modeling techniques with input-space warping is proposed for the cost-efficient construction of a stochastic surrogate model. The framework achieves high computational efficiency by combining a large number of cheap estimates with a few expensive measurements. It is based on coarse-grid approximations of high-fidelity numerical simulations. The framework is applied to generate a surrogate model for crystal growth velocities in directional dendritic solidification, and its accuracy is assessed using cross-validation techniques.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Mechanical
Kunpeng Li, Xiwang He, Liye Lv, Jiaxiang Zhu, Guangbo Hao, Haiyang Li, Xueguan Song
Summary: This paper proposes a single-fidelity surrogate model with a hierarchical structure called Nonlinearity Integrated Correlation Mapping Surrogate (NI-CMS) model, which overcomes the expensive modeling cost by establishing a low-fidelity model and correcting it using the idea of a multi-fidelity surrogate model. The performance of the proposed model is evaluated through numerical test functions and a surrogate-based digital twin experiment, demonstrating its advantages over existing models.
JOURNAL OF MECHANICAL DESIGN
(2023)
Article
Computer Science, Interdisciplinary Applications
Meng Cheng, Ping Jiang, Jiexiang Hu, Leshi Shu, Qi Zhou
Summary: A multi-fidelity surrogate modeling method based on variance-weighted sum (VWS-MFS) was developed in this study to address the challenge of handling multiple non-hierarchical low-fidelity data. This method allocates diverse weights to each set of data using uncertainties quantified by variances of Kriging models, allowing all low-fidelity data to contribute to the trend function reflecting the response trend of the true model. Numerical examples and an engineering case comparison demonstrated that the proposed VWS-MFS method provides more accurate surrogate models at lower computational costs.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Aerospace
Hongyan Bu, Liming Song, Zhendong Guo, Jun LI
Summary: This paper investigates the accuracy issue of the Maximum Likelihood Estimation (MLE) in selecting the scale factor for the Bayesian Multi-Fidelity Surrogate (MFS) model. An alternative approach is proposed to select the scale factor by minimizing the posterior variance of the discrepancy function. Experimental results demonstrate that the proposed approach achieves better accuracy and robustness compared to MLE.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Multidisciplinary
Paolo Conti, Mengwu Guo, Andrea Manzoni, Jan S. Hesthaven
Summary: When evaluating quantities of interest dependent on differential equation solutions, there is a trade-off between accuracy and efficiency. The proposed multi-fidelity surrogate modeling approach leverages low-fidelity data to improve approximations with limited high-fidelity data, using long short-term memory (LSTM) networks for parameterized, time-dependent problems. This approach enhances output predictions for unseen parameter values and forward in time simultaneously, and outperforms feed-forward neural networks in terms of accuracy.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Qi Guo, Jiutao Hang, Suian Wang, Wenzhi Hui, Zonghong Xie
Summary: This paper presents an efficient design optimization method assisted by multi-fidelity surrogate models for buckling design of variable stiffness composites. By using hierarchical Kriging and global optimization method, the effectiveness and robustness of the method are demonstrated through two case studies.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Aerospace
Quan Lin, Jiexiang Hu, Qi Zhou
Summary: This paper proposes two parallel multi-objective Bayesian optimization (MOBO) approaches based on multi-fidelity surrogate modeling to improve the optimization efficiency. The approaches utilize cheap auxiliary low-fidelity data for better performance. The modified hypervolume expected improvement function is used to determine the updating points and fidelity levels, and two parallel computing strategies are developed for multi-point sampling. Additionally, a constraint handling strategy is introduced for constrained problems. The proposed approaches are validated through numerical benchmark examples and real-world applications, showing significant improvements in terms of efficiency and overall performance compared to state-of-the-art MOBO methods.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Marine
Xinwang Liu, Weiwen Zhao, Decheng Wan
Summary: This paper introduces the application of the multi-fidelity Co-Kriging surrogate model in hull form optimization design, which combines the advantages of high-fidelity and low-fidelity sample evaluation to improve accuracy and efficiency. Numerical examples are used to validate the advantages of the multi-fidelity Co-Kriging surrogate model in terms of fidelity and efficiency.
Article
Computer Science, Interdisciplinary Applications
Haizhou Yang, Seong Hyeong Hong, Yi Wang
Summary: This paper presents a novel computation-aware multi-fidelity surrogate-based optimization methodology and a new sequential and adaptive sampling strategy based on expected improvement reduction. It improves the exploration and convergence rate of the optimization process under a fixed computational budget.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Chao Zhang, Lixue Liu, Hao Wang, Xueguan Song, Dacheng Tao
Summary: This paper proposes a stacking-based conditional generative adversarial network (SCGAN) for multi-fidelity surrogate (MFS) modeling. By integrating high-fidelity and low-fidelity data, SCGAN accurately and stably approximates the responses of a physical system. Experimental results demonstrate that SCGAN performs better than existing MFS modeling methods for varying high-fidelity sample sizes.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Multidisciplinary
Cho Mar Aye, Kittinan Wansaseub, Sumit Kumar, Ghanshyam G. Tejani, Sujin Bureerat, Ali R. Yildiz, Nantiwat Pholdee
Summary: This work presents a multi-fidelity multi-objective infill-sampling surrogate-assisted optimization method for airfoil shape optimization. Computational Fluid Dynamic (CFD) and XFoil tools are used to find the real objective function value. A special multi-objective sub-optimization problem is proposed to improve the surrogate model constructed. The results show that the proposed technique is the best performer for the demonstrated airfoil shape optimization.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Jun Liu, Jiaxiang Yi, Qi Zhou, Yuansheng Cheng
Summary: In this work, a novel sequential multi-fidelity surrogate model-assisted contour prediction method is developed. An extended expected improvement (EEI) infill criterion is proposed to determine the locations and fidelity level of new samples, and a parallel strategy is used to generate low-fidelity (LF) samples. The proposed approach shows better efficiency, prediction accuracy, and robust performance compared to state-of-the-art methods.
ENGINEERING WITH COMPUTERS
(2022)
Article
Chemistry, Physical
Attasit Wiangkham, Prasert Aengchuan, Rattanaporn Kasemsri, Auraluck Pichitkul, Suradet Tantrairatn, Atthaphon Ariyarit
Summary: Artificial intelligence plays a significant role in solving complex problems, including fracture mechanics. By combining experimental data with fracture criteria data, an artificial intelligence model was created, resulting in more accurate predictions compared to using only experimental data.