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

Zhang, Enrui; Dao, Ming; Karniadakis, George Em; Suresh, Subra

doi:10.1126/sciadv.abk0644

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

Journal Article · 16 February 2022 · Science Advances

DOI: https://doi.org/10.1126/sciadv.abk0644 · OSTI ID:1904769

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Brown University, Providence, RI (United States); Nanyang Technological Univ., Singapore (Singapore)
Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
Brown University, Providence, RI (United States)
Nanyang Technological University (Singapore)

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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Research Organization:: Brown University, Providence, RI (United States)

Sponsoring Organization:: Air Force Office of Scientific Research (AFOSR); Nanyang Technological University; USDOE Office of Science (SC)

OSTI ID:: 1904769

Journal Information:: Science Advances, Journal Name: Science Advances Journal Issue: 7 Vol. 8; ISSN 2375-2548

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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