{"title":"Learning Dynamics of Nonlinear Field-Circuit Coupled Problems With a Physics-Data Combined Model","authors":"Shiqi Wu, Gérard Meunier, Olivier Chadebec, Qianxiao Li, Ludovic Chamoin","doi":"10.1002/nme.70015","DOIUrl":null,"url":null,"abstract":"<p>This work introduces a combined model that integrates a linear state-space model with a Koopman-type machine-learning model to efficiently predict the dynamics of nonlinear, high-dimensional, and field-circuit coupled systems, as encountered in areas such as electromagnetic compatibility, power electronics, and electric machines. Using an extended nonintrusive model combination algorithm, the proposed model achieves high accuracy with an error of approximately 1%, outperforming baselines: a state-space model and a purely data-driven model. Moreover, it delivers a computational speed-up of three orders of magnitude compared with the traditional time-stepping volume integral method on the same mesh in the online prediction stage, at the cost of a one-time training effort and previously mentioned error, making it highly effective for real-time and repeated predictions.</p>","PeriodicalId":13699,"journal":{"name":"International Journal for Numerical Methods in Engineering","volume":"126 5","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nme.70015","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Numerical Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/nme.70015","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This work introduces a combined model that integrates a linear state-space model with a Koopman-type machine-learning model to efficiently predict the dynamics of nonlinear, high-dimensional, and field-circuit coupled systems, as encountered in areas such as electromagnetic compatibility, power electronics, and electric machines. Using an extended nonintrusive model combination algorithm, the proposed model achieves high accuracy with an error of approximately 1%, outperforming baselines: a state-space model and a purely data-driven model. Moreover, it delivers a computational speed-up of three orders of magnitude compared with the traditional time-stepping volume integral method on the same mesh in the online prediction stage, at the cost of a one-time training effort and previously mentioned error, making it highly effective for real-time and repeated predictions.
期刊介绍:
The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems.
The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.