{"title":"反应扩散建模中有限元方法的发展","authors":"Rohit Sharma, Om Prakash Yadav","doi":"10.1007/s11831-025-10222-x","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents a comprehensive review of finite element methods (FEMs) applied to a diverse range of reaction-diffusion equations (RDEs). Beginning with a historical overview of FEMs, we then provide a summary of various FEMs, including standard Galerkin (both conforming and non-conforming), mixed Galerkin, discontinuous Galerkin, and weak Galerkin. Additionally, a priori and a posteriori error have been discussed for standard Galerkin. In further discussion related to RDEs, we provide insights into the evolution of these equations and their significance in various fields. We then systematically review these FEMs for solving different types of RDEs, including more recent advances pertaining to RDEs with nonlinear reaction terms, and advection reaction-diffusion equations. Finally, we briefly highlight the applications of machine learning and deep neural networks to FEM.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 5","pages":"2745 - 2766"},"PeriodicalIF":12.1000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Evolution of Finite Element Approaches in Reaction-Diffusion Modeling\",\"authors\":\"Rohit Sharma, Om Prakash Yadav\",\"doi\":\"10.1007/s11831-025-10222-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents a comprehensive review of finite element methods (FEMs) applied to a diverse range of reaction-diffusion equations (RDEs). Beginning with a historical overview of FEMs, we then provide a summary of various FEMs, including standard Galerkin (both conforming and non-conforming), mixed Galerkin, discontinuous Galerkin, and weak Galerkin. Additionally, a priori and a posteriori error have been discussed for standard Galerkin. In further discussion related to RDEs, we provide insights into the evolution of these equations and their significance in various fields. We then systematically review these FEMs for solving different types of RDEs, including more recent advances pertaining to RDEs with nonlinear reaction terms, and advection reaction-diffusion equations. Finally, we briefly highlight the applications of machine learning and deep neural networks to FEM.</p></div>\",\"PeriodicalId\":55473,\"journal\":{\"name\":\"Archives of Computational Methods in Engineering\",\"volume\":\"32 5\",\"pages\":\"2745 - 2766\"},\"PeriodicalIF\":12.1000,\"publicationDate\":\"2025-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Computational Methods in Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11831-025-10222-x\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Computational Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11831-025-10222-x","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
The Evolution of Finite Element Approaches in Reaction-Diffusion Modeling
This paper presents a comprehensive review of finite element methods (FEMs) applied to a diverse range of reaction-diffusion equations (RDEs). Beginning with a historical overview of FEMs, we then provide a summary of various FEMs, including standard Galerkin (both conforming and non-conforming), mixed Galerkin, discontinuous Galerkin, and weak Galerkin. Additionally, a priori and a posteriori error have been discussed for standard Galerkin. In further discussion related to RDEs, we provide insights into the evolution of these equations and their significance in various fields. We then systematically review these FEMs for solving different types of RDEs, including more recent advances pertaining to RDEs with nonlinear reaction terms, and advection reaction-diffusion equations. Finally, we briefly highlight the applications of machine learning and deep neural networks to FEM.
期刊介绍:
Archives of Computational Methods in Engineering
Aim and Scope:
Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication.
Review Format:
Reviews published in the journal offer:
A survey of current literature
Critical exposition of topics in their full complexity
By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.