超弹性材料
非线性系统
空隙(复合材料)
线弹性
人工神经网络
可微函数
表征(材料科学)
本构方程
反问题
计算机科学
材料性能
材料科学
数学
数学分析
物理
人工智能
结构工程
有限元法
纳米技术
工程类
复合材料
量子力学
作者
Enrui Zhang,Ming Dao,George Em Karniadakis,S. Suresh
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2022-02-18
卷期号:8 (7)
被引量:77
标识
DOI:10.1126/sciadv.abk0644
摘要
Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.
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