| [1] JIN S S, KIM S T, PARK Y H. Combining point and distributed strain sensor for complementary data-fusion: A multi-fidelity approach[J]. Mechanical Systems and Signal Processing, 2021, 157: 107725.[2] KIM S, CHOI J-H, KIM NH. Data-driven prognostics with low-fidelity physical information for digital twin: physics-informed neural network[J]. Structural and Multidisciplinary Optimization, 2022, 65(9): 1-16.[3] LI L, LEI B, MAO C. Digital twin in smart manufac-turing[J]. Journal of Industrial Information Integration. 2022, 26, 100289.[4] LI C, MAHADEVAN S, LING Y, et al. Dynamic bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3): 930-941.[5] LIM KYH, ZHENG P, CHEN C-H, et al. A digital twin-enhanced system for engineering product family design and optimization[J]. Journal of Manufacturing Systems, 2020, 57: 82-93.[6] TAO F, ZHANG H, LIU A, et al. Digital twin in indus-try: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415.[7] WANG S, LAI X, HE X, et al. Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate model[J]. Journal of Me-chanical Design, 2022, 144(3): 031703.[8] XIA M, SHAO H, WILLIAMS D, et al. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning[J]. Reliability Engineering & System Safety, 2021, 215: 107938.[9] GHOSH M, WU L, HAO Q, et al. A random forest with multi-fidelity Gaussian process leaves for model-ing multi-fidelity data with heterogeneity[J]. Comput-ers & Industrial Engineering, 2022, 174: 108746.[10] 李增聪, 田阔, 赵海心. 面向多级加筋壳的高效变保真度代理模型[J]. 航空学报, 2020, 41(7): 623435-623435.LI Z C, TIAN K, ZHAO H X. Efficient variable-fidelity models for hierarchical stiffened shells[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7): 623435-623435 (in Chinese).[11] TIAN K, LI ZC, HUANG L, et al. Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering[J]. Computer Methods in Applied Mechanics and Engi-neering, 2020, 366: 113045.[12] LI ZC, ZHANG S, LI H, et al. On-line transfer learn-ing for multi-fidelity data fusion with ensemble of deep neural networks[J]. Advanced Engineering Informatics, 2022, 53: 101689.[13] CHEN J, MENG C, GAO Y, et al. Multi-fidelity neural optimization machine for Digital Twins[J]. Structural and Multidisciplinary Optimization, 2022, 65(12): 1-15.[14] LI K, WANG S, LIU Y, et al. An integrated surrogate modeling method for fusing noisy and noise-free da-ta[J]. Journal of Mechanical Design, 2022, 146(6): 061701.[15] LAI X, HE X, PANG Y, et al. A scalable digital twin framework based on a novel adaptive ensemble surro-gate model[J]. Journal of Mechanical Design, 2023, 145(2): 021701.[16] NATEKIN A, KNOLL A. Gradient boosting machines, a tutorial[J]. Frontiers in neurorobotics, 2013, 7: 21.[17] TING K M, WITTEN I H. Stacking bagged and dagged models[J], 1997.[18] GANAIE M A, HU M, MALIK A K, et al. Ensemble deep learning: A review[J]. Engineering Applications of Artificial Intelligence, 2022, 115: 105151.[19] GUNN S R. Support vector machines for classification and regression[J]. ISIS technical report, 1998, 14(1): 5-16.[20] TIAN K, LI ZC, ZHANG JX, et al. Transfer learning based variable-fidelity surrogate model for shell buck-ling prediction[J]. Composite Structures, 2021, 273: 114285.[21] GISELLE FERNáNDEZ-GODINO M, PARK C, KIM NH, et al. Review of multi-?delity models[J]. arXiv preprint arXiv:160907196, 2016.[22] ZHOU Q, SHAO XY, JIANG P, et al. An active learn-ing metamodeling approach by sequentially exploiting difference information from variable-fidelity models[J]. Advanced Engineering Informatics, 2016, 30(3): 283-297.[23] TIAN K, WANG B, ZHANG K, et al. Tailoring the optimal load-carrying efficiency of hierarchical stiff-ened shells by competitive sampling[J]. Thin-Walled Structures, 2018, 133: 216-225. |
CJA
