Article(id=1309639272714559671, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639270319616416, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20240132, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171934535, onlineDateStr=2026-09-23, pubDate=1775318400000, pubDateStr=2026-04-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171934535, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171934535, creator=13701087609, updateTime=1790171934535, updator=13701087609, issue=Issue{id=1309639270319616416, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1775318400000', pubDateStr='2026-04-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171933965, creator='13701087609', updateTime=1790233801014, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309898759593623948, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639270319616416, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309898759593623949, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639270319616416, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=22, ext={EN=ArticleExt(id=1309639272882331832, articleId=1309639272714559671, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Research on Model Order Reduction Applied on Clutch and Hydraulic System of Hybrid Electric Vehicles'Transmission Based on Differential Neural Network, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To enhance the computational speed of the hybrid transmission clutch and hydraulic system model, this paper employs the Neural Ordinary Differential Equations (NODEs) to reduce the order of the physical model. Initially, a pseudo-random number method is utilized to generate model inputs and obtain simulation results. Subsequently, the time-series data of system state variables are extracted as samples to train the NODEs model. The comparison between the physical model and the NODEs model reveals that the latter possesses excellent precision and generalization capabilities, significantly enhancing computational speed.

, authors=Xinglei Shi, Lei Yang, Anwei Zhang, Wentai Zhou, Yuan Jia, Yi Chen, authorsList=Xinglei Shi, Lei Yang, Anwei Zhang, Wentai Zhou, Yuan Jia, Yi Chen, authorCompany=null, correspAuthors=null, 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=1309639273062686905, articleId=1309639272714559671, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于微分神经网络的混动变速器离合器及液压系统模型降阶方法研究, columnId=0, journalTitle=汽车文摘, columnName=, runingTitle=null, highlight=null, articleAbstract=

为了提高混合动力变速器离合器和液压系统模型的计算速度，采用神经微分方程（NODEs）对物理模型进行降阶。首先，利用伪随机数方法生成模型输入并获得仿真结果。随后，提取系统状态变量的时间序列数据作为样本训练神经微分方程模型。物理模型和NODEs模型的比较结果表明，后者具有出色的精度和泛化能力，并显著提升计算速度。

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