Article(id=1311360872216359148, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769443200000, receivedDateStr=2026-01-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582395842, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582395842, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582395842, creator=13701087609, updateTime=1790582395842, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=21, endPage=29, ext={EN=ArticleExt(id=1311360872400908525, articleId=1311360872216359148, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Identification of ship maneuvering motion response model based on physics-informed neural networks, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

With the development of intelligent shipping and autonomous ship maneuvering technologies, higher requirements were imposed on the parameter identification accuracy of ship motion models in practical navigation environments. However, under limited sample conditions, traditional parameter identification methods for nonlinear ship motion models were difficult to achieve both accuracy and stability, which restricted their engineering applications. To address this problem, a ship maneuvering parameter identification method based on Physics-Informed Neural Networks (PINN) was proposed. During network training, a second-order nonlinear ship maneuvering response model was deeply introduced as a physical prior constraint, and the dynamic information of the model was deeply integrated with limited observation data to improve the effectiveness of parameter learning under limited sample conditions. Numerical simulations of 20°/20° zigzag maneuvering tests were carried out based on the Mariner model ship, and training and validation datasets were constructed to identify ship maneuverability indices. The identification results were compared with those obtained by the Extended Kalman Filter (EKF) method and the Least-Squares Support Vector Machine (LS-SVM) method. The results show that, under limited sample conditions, the proposed method improves the identification accuracy of nonlinear ship maneuvering model parameters by 14.75% and 7.74% compared with the EKF method and the LS-SVM method, respectively. In robustness tests with Gaussian white noise contamination, stable parameter identification performance is maintained by the PINN-based method, and the minimum identification error reaches 0.05%. The results indicate that the proposed method effectively solves the problem of high-precision parameter identification for nonlinear ship maneuvering models under limited data conditions and provides a feasible technical approach for ship motion modeling and maneuvering performance analysis.

, authors=Yongfeng Suo, Song Zhang, Weijun Wang^*, Lei Cui, Hongbo Sun, Gaocai Li, authorsList=Yongfeng Suo, Song Zhang, Weijun Wang, Lei Cui, Hongbo Sun, Gaocai Li, authorCompany=null, correspAuthors=Weijun Wang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360872677732590, articleId=1311360872216359148, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于物理信息神经网络的船舶运动响应模型辨识, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

随着智能航运与船舶自主操纵技术的发展，船舶运动模型在实际航行环境中对参数辨识精度提出了更高要求。然而，在样本数量受限的条件下，传统船舶非线性运动模型参数辨识方法难以兼顾精度与稳定性，制约了其工程应用效果。针对上述问题，提出一种基于物理信息神经网络（PINN）的船舶运动操纵参数辨识方法。该方法在神经网络训练过程中引入船舶二阶非线性响应模型作为物理先验约束，将模型动力学信息与有限观测数据进行深度融合，从而提升有限样本条件下参数学习的有效性。基于Mariner船模开展20°/20°Z形操纵试验的数值仿真，构建训练与验证数据集，对船舶操纵性指数进行辨识，并将结果与扩展卡尔曼滤波（EKF）方法和最小二乘支持向量机（LS-SVM）方法进行对比分析。结果表明，在有限样本数据条件下，所提方法在船舶非线性响应模型参数辨识精度方面较EKF和LS-SVM方法分别提升了14.75%和7.74%。在加入高斯白噪声的鲁棒性试验中，PINN仍能保持稳定的参数辨识性能，最小辨识误差可达0.05%。研究结果表明，该方法能够有效解决有限数据条件下船舶非线性响应模型参数高精度辨识问题，为船舶运动建模与操纵性能分析提供了一种可行的技术途径。

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索永峰（1981—），男，教授，研究方向为海事地理信息系统、航海仿真，（E-mail）yfsuo@jmu.edu.cn

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索永峰（1981—），男，教授，研究方向为海事地理信息系统、航海仿真，（E-mail）yfsuo@jmu.edu.cn

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