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Accelerating three-dimensional phase-field simulations via deep learning approaches

文献类型：期刊论文

作者Zhou, Xuewei5,6; Sun, Sheng3; Cai, Songlin1; Chen, Gongyu6; Wu, Honghui2,4; Xiong, Jie3; Zhu, Jiaming2,5,6; Cai SL(蔡松林)
刊名JOURNAL OF MATERIALS SCIENCE
出版日期2024-09-01
卷号59期号:33页码:15727-15737
ISSN号0022-2461
DOI10.1007/s10853-024-10118-4
通讯作者Wu, Honghui(wuhonghui@ustb.edu.cn) ; Xiong, Jie(xiongjie@shu.edu.cn) ; Zhu, Jiaming(zhujiaming@sdu.edu.cn)
英文摘要Phase-field modeling (PFM) is a powerful but computationally expensive technique for simulating three-dimensional (3D) microstructure evolutions. Very recently, integrating machine learning into phase-field simulations provides a promising way to reduce calculation time remarkably. In this study, we propose a deep learning model that combines a convolutional autoencoder with a deep operator network to predict 3D microstructure evolution by using 2D slices of the 3D system. It is found that the deep learning model can shorten the calculation time from 37 min to 3 s after the initial training, while skipping 5-time steps, and reduce the phase-field simulation time by 31% in entire calculation of the evolution process. Interestingly, this model achieves good accuracy in predicting 3D microstructures by utilizing only 2D information. This work demonstrates the efficiency of machine learning in accelerating phase-field simulations while maintaining high accuracy and promotes the application of PFM in fundamental studies.
WOS关键词ALLOYS ; MODEL ; CAHN ; RECRYSTALLIZATION ; APPROXIMATION ; NETWORK
资助项目National Natural Science Foundation of China[12372152] ; National Natural Science Foundation of China[52122408] ; National Natural Science Foundation of China[52071023] ; National Natural Science Foundation of China[12072179] ; Qilu Young Talent Program of Shandong University, Zhejiang Lab Open Research Project[K2022PE0AB05] ; Shandong Provincial Natural Science Foundation[ZR2023MA058] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515011819] ; Guangdong Basic and Applied Basic Research Foundation[2024A1515012469]
WOS研究方向Materials Science
语种英语
WOS记录号WOS:001297590500002
资助机构National Natural Science Foundation of China ; Qilu Young Talent Program of Shandong University, Zhejiang Lab Open Research Project ; Shandong Provincial Natural Science Foundation ; Guangdong Basic and Applied Basic Research Foundation
源URL[http://dspace.imech.ac.cn/handle/311007/96384]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Wu, Honghui; Xiong, Jie; Zhu, Jiaming
作者单位1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
2.Liaoning Acad Mat, Inst Mat Intelligent Technol, Shenyang 110004, Peoples R China
3.Shanghai Univ, Mat Genome Inst, Shanghai 200444, Peoples R China
4.Univ Sci & Technol Beijing, Inst Carbon Neutral, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China
5.Shandong Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
6.Shandong Univ, Sch Civil Engn, Jinan 250061, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Xuewei,Sun, Sheng,Cai, Songlin,et al. Accelerating three-dimensional phase-field simulations via deep learning approaches[J]. JOURNAL OF MATERIALS SCIENCE,2024,59(33):15727-15737.
APA Zhou, Xuewei.,Sun, Sheng.,Cai, Songlin.,Chen, Gongyu.,Wu, Honghui.,...&蔡松林.(2024).Accelerating three-dimensional phase-field simulations via deep learning approaches.JOURNAL OF MATERIALS SCIENCE,59(33),15727-15737.
MLA Zhou, Xuewei,et al."Accelerating three-dimensional phase-field simulations via deep learning approaches".JOURNAL OF MATERIALS SCIENCE 59.33(2024):15727-15737.

入库方式： OAI收割

来源：力学研究所

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