{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T04:23:46Z","timestamp":1788927826860,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Institute of Electrical Engineering, CAS","award":["Y960211CS3, E155620101, E139620101"],"award-info":[{"award-number":["Y960211CS3, E155620101, E139620101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accuracy and efficiency of traditional solution methods, such as the finite element method (FEM), are tightly related to the quality and density of mesh construction. Recently, PINN (physics-informed neural network) was proposed as a method for solving differential equations. This method is mesh free and generalizes the process of solving PDEs regardless of the equations\u2019 structure. Therefore, an experiment is conducted to explore the feasibility of PINN in solving electro-thermal coupling problems, which include the electrokinetic field and steady-state thermal field. We utilize two neural networks in the form of sequential training to approximate the electric field and the thermal field, respectively. The experimental results show that PINN provides good accuracy in solving electro-thermal coupling problems.<\/jats:p>","DOI":"10.3390\/a15020053","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T09:59:21Z","timestamp":1643709561000},"page":"53","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Preliminary Study on the Resolution of Electro-Thermal Multi-Physics Coupling Problem Using Physics-Informed Neural Network (PINN)"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4901-3534","authenticated-orcid":false,"given":"Yaoyao","family":"Ma","sequence":"first","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Beijing Key Laboratory of Three-Dimensional and Nanometer Integrated Circuit Design Automation Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9130-9657","authenticated-orcid":false,"given":"Xiaoyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoxiang","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Group of Electrical and Electronic Engineering of Paris, Sorbonne Universit\u00e9, Universit\u00e9 Paris-Saclay, CentraleSup\u00e9lec, CNRS, 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.neucom.2021.06.015","article-title":"Extreme Theory of Functional Connections: A Fast Physics-Informed Neural Network Method for Solving Ordinary and Partial Differential Equations","volume":"457","author":"Schiassi","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/JBHI.2016.2526603","article-title":"HEp-2 Cell Image Classification with Deep Convolutional Neural Networks","volume":"21","author":"Gao","year":"2016","journal-title":"IEEE J. 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