[1] N UMEDA. Nonlinear dynamics of ship capsizing due to broaching in following and quartering seas. Journal of Marine Science and Technology, 4, 16-26(1999).

[2] R SKEJIC, O M FALTINSEN. A unified seakeeping and maneuvering analysis of ships in regular waves. Journal of Marine Science and Technology, 13, 371-394(2008).

[3] W ZHANG, Z J ZOU. Time domain simulations of the wave-induced motions of ships in maneuvering condition. Journal of Marine Science and Technology, 21, 154-166(2016).

[4] J H WANG, D C WAN. CFD investigations of ship maneuvering in waves using NAOE-FOAM-SJTU solver. Journal of Marine Science and Application, 17, 443-458(2018).

[5] D KIM, S SONG, T TEZDOGAN. Free running CFD simulations to investigate ship manoeuvrability in waves. Ocean Engineering, 236, 109567(2021).

[6] B S KIM, S G WANG, Z ZHU et al. Numerical simulation of free-running turning test of ship in waves. Ocean Engineering, 288, 115951(2023).

[7] H YASUKAWA. Simulations of ship maneuvering in waves: 2nd report: zig-zag and stopping maneuvers. Journal of the Japan Society of Naval Architects and Ocean Engineers, 7, 163-170(2008).

[8] H YASUKAWA, M A A HASNAN, A MATSUDA. Validation of 6-DOF motion simulations for ship turning in regular waves. Journal of Marine Science and Technology, 26, 1096-1111(2021).

[9] Y SANADA, H ELSHIEKH, Y TODA et al. ONR tumblehome course keeping and maneuvering in calm water and waves. Journal of Marine Science and Technology, 24, 948-967(2019).

[10] [10] RAVISEKHAR RADHAKRISHNA K O S R, PANNEER SELVAM R. Estimation of hydrodynamic derivatives from sea trial data using system identification technique[C]Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018). Singape: Springer, 2019, 22: 169−185.

[11] H T XU, SOARES C GUEDES. Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated LS-SVM. Ocean Engineering, 191, 106488(2019).

[12] M ZHU, W Q SUN, A HAHN et al. Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robust to outliers. Ocean Engineering, 200, 107053(2020).

[13] L Z HAO, Y HAN, C SHI et al. Recurrent neural networks for nonparametric modeling of ship maneuvering motion. International Journal of Naval Architecture and Ocean Engineering, 14, 100436(2022).

[14] K WAKITA, A MAKI, N UMEDA et al. On neural network identification for low-speed ship maneuvering model. Journal of Marine Science and Technology, 27, 772-785(2022).

[15] J K LOU, H D WANG, J Y WANG et al. Deep learning method for 3-DOF motion prediction of unmanned surface vehicles based on real sea maneuverability test. Ocean Engineering, 250, 111015(2022).

[16] G CHEN, W WANG, Y F XUE. Identification of ship dynamics model based on sparse Gaussian process regression with similarity. Symmetry, 13, 1956(2021).

[17] X F ZHANG, Y MENG, Z C LIU et al. Modified grey wolf optimizer-based support vector regression for ship maneuvering identification with full-scale trial. Journal of Marine Science and Technology, 27, 576-588(2022).

[18] R RAI, C K SAHU. Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus. IEEE Access, 8, 71050-71073(2020).

[19] RUEDEN L VON, S MAYER, K BECKH et al. Informed machine learning-A taxonomy and survey of integrating prior knowledge into learning systems. IEEE Transactions on Knowledge and Data Engineering, 35, 614-633(2023).

[20] Y Y WEI, Z Z CHEN, C ZHAO et al. Big multi-step ship motion forecasting using a novel hybrid model based on real-time decomposition, boosting algorithm and error correction framework. Ocean Engineering, 256, 111471(2022).

[21] B MEI, L C SUN, G Y SHI et al. Ship maneuvering prediction using grey box framework via adaptive RM-SVM with minor rudder. Polish Maritime Research, 26, 115-127(2019).

[22] B MEI, L C SUN, G Y SHI. White-black-box hybrid model identification based on RM-RF for ship maneuvering. IEEE Access, 7, 57691-57705(2019).

[23] R E NIELSEN, D PAPAGEORGIOU, L NALPANTIDIS et al. Machine learning enhancement of manoeuvring prediction for ship digital twin using full-scale recordings. Ocean Engineering, 257, 111579(2022).

[24] [24] HAN Y, HAO L Z, SHI C, et al. Prediction of ship maneuvering motion with greybox modelling incpating mechanism data[JOL]. Ships Offshe Structures, 2023: 1−14. https:www.tfonline.comdoifull10.108017445302.2023.2231207.

[26] [26] ABKOWITZ M A. Lectures on ship hydrodynamicssteering manoeuvrability[R]. Lyngby, Denmark: Hydro Aerodynamics Labaty, 1964.

[27] [27] LETIZIA L. Damage survivability of passenger ships in a seaway[D]. Glasgow, Scotl: University of Strathclyde, 1996.

[28] [28] RUMELHART D E, MCCLELL J L. Parallel distributed processing: explations in the microstructure of cognition, Ⅰ&Ⅱ[M]. Cambridge: MIT Press, 1986.

[29] [29] JIANG L, SIGNAL S, JEFFRIES B, et al. A hydrodynamic digital twin concept f underwater vehicles[C]33rd Symposium on Naval Hydrodynamics. Osaka, Japan, 2020: 111.

[30] M ARAKI, H SADAT-HOSSEINI, Y SANADA et al. Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data. Ocean Engineering, 51, 63-84(2012).

[31] N UMEDA, S YAMAMURA, A MATSUDA et al. Model experiments on extreme motions of a wave-piercing tumblehome vessel in following and quartering waves. Journal of the Japan Society of Naval Architects and Ocean Engineers, 8, 123-129(2008).