[3] J KUDELA, R MATOUSEK. Recent advances and applications of surrogate models for finite element method computations: a review. Soft Computing, 26, 13709-13733(2022).

[5] A FREENY. Empirical model building and response surfaces. Technometrics, 30, 229-231(1987).

[6] D G KRIGE. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the South African Institute of Mining and Metallurgy, 52, 201-203(1951).

[7] D S BROOMHEAD, D LOWE. Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355(1988).

[8] X X FAN, P F WANG, F F HAO. Reliability-based design optimization of crane bridges using Kriging-based surrogate models. Structural and Multidisciplinary Optimization, 59, 993-1005(2019).

[9] L CHU, J J SHI, CURSI E S DE. Kriging surrogate model for resonance frequency analysis of dental implants by a Latin hypercube-based finite element method. Applied Bionics and Biomechanics, 2019, 3768695(2019).

[10] A K S O HASSAN, A S ETMAN, E A SOLIMAN. Optimization of a novel Nano antenna with two radiation modes using Kriging surrogate models. IEEE Photonics Journal, 10, 4800807(2018).

[11] A HAERI, M J FADAEE. Efficient reliability analysis of laminated composites using advanced Kriging surrogate model. Composite Structures, 149, 26-32(2016).

[12] J J SHI, L CHU, R BRAUN. A Kriging surrogate model for uncertainty analysis of graphene based on a finite element method. International Journal of Molecular Sciences, 20, 2355(2019).

[13] L LADICKÝ, S JEONG, B SOLENTHALER et al. Data-driven fluid simulations using regression forests. ACM Transactions on Graphics, 34, 199(2015).

[15] Q WANG, W Q QIAN, K F HE. Unsteady aerodynamic modeling at high angles of attack using support vector machines. Chinese Journal of Aeronautics, 28, 659-668(2015).

[16] KAJBAF A AL, M BENSI. Application of surrogate models in estimation of storm surge: a comparative assessment. Applied Soft Computing, 91, 106184(2020).

[17] T BAI, P TAHMASEBI. Characterization of groundwater contamination: a transformer-based deep learning model. Advances in Water Resources, 164, 104217(2022).

[18] X Y ZHAO, Z Q GONG, J ZHANG et al. A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout. Structural and Multidisciplinary Optimization, 64, 2287-2306(2021).

[19] [19] SHIBATA R, OHIRA M, MA Z W. A novel convolutionalautoencoder based surrogate model f fast Sparameter calculation of planar BPFs[C]2022 IEEEMTTS International Microwave Symposium IMS 2022. Denver: IEEE, 2022: 498501.

[20] J M ZHOU, L L DONG, W GUAN et al. Impact load identification of nonlinear structures using deep recurrent neural network. Mechanical Systems and Signal Processing, 133, 106292(2019).

[21] T CHEN, L GUO, A D Z DUAN et al. A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure. Structural Health Monitoring, 21, 1590-1607(2022).

[22] S HOCHREITER, J SCHMIDHUBER. Long short-term memory. Neural Computation, 9, 1735-1780(1997).