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Physics-informed neural networks for phase-based material defect identification

文献类型：期刊论文

作者He, Haoshen1; 刘洋1,2)
刊名SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
出版日期2025-08-01
卷号68期号:9页码:14
关键词defect identification PINNs phase field inverse problem
ISSN号1674-7348
DOI10.1007/s11433-025-2692-7
通讯作者Liu, Yang(liuyang22@ucas.ac.cn)
英文摘要The accurate identification of material defects is critical for ensuring structural integrity and performance. Traditional computational methods often struggle to balance efficiency and physical fidelity in complex material systems. This paper presents a novel approach integrating physics-informed neural networks (PINNs) with the phase field method to address these challenges. Our approach leverages a phase field variable to delineate intact regions from voids, while a stress degradation model modifies mechanical responses at defect sites. Neural networks serve as surrogate forward solvers to predict displacement and stress fields, enabling rapid simulations. To ensure compatibility with physical laws, the framework embeds governing equations into the training loss function. Additionally, a data-driven term minimizes discrepancies between simulated and experimentally measured strain fields, enhancing defect localization precision. Numerical experiments validate the framework's robustness across diverse configurations, including circular, elliptical, irregular, and multiple voids, as well as material behaviors, extending from linear elastic to hyperelastic models. The results demonstrate superior accuracy in identifying void geometry, size, and spatial distribution compared to conventional methods. The proposed approach's adaptability to complex geometries and material nonlinearities highlights its broad applicability in aerospace, automotive, and biomedical industries.
分类号一类
WOS关键词DEEP LEARNING FRAMEWORK ; FLAWS ; XFEM
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0620103] ; Fundamental Research Funds for the Central Universities[E2EG2202X2]
WOS研究方向Physics
语种英语
WOS记录号WOS:001545129600001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities
其他责任者刘洋
源URL[http://dspace.imech.ac.cn/handle/311007/102187]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 101408, Peoples R China;
2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100090, Peoples R China
推荐引用方式
GB/T 7714		 He, Haoshen,刘洋1,2). Physics-informed neural networks for phase-based material defect identification[J]. SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,2025,68(9):14.
APA He, Haoshen,&刘洋1,2).(2025).Physics-informed neural networks for phase-based material defect identification.SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,68(9),14.
MLA He, Haoshen,et al."Physics-informed neural networks for phase-based material defect identification".SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY 68.9(2025):14.

入库方式： OAI收割

来源：力学研究所

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