Analyses of internal structures and defects in materials using physics-informed neural networks.

ABSTRACT: 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

SUBMITTER: Zhang E 

PROVIDER: S-EPMC8849303 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature