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Title: Analyses of internal structures and defects in materials using physics-informed neural networks
Authors: Zhang, Enrui
Dao, Ming
Karniadakis, George Em
Suresh, Subra
Keywords: Engineering::Materials
Issue Date: 2022
Source: Zhang, E., Dao, M., Karniadakis, G. E. & Suresh, S. (2022). Analyses of internal structures and defects in materials using physics-informed neural networks. Science Advances, 8(7), eabk0644-.
Journal: Science Advances 
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 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.
ISSN: 2375-2548
DOI: 10.1126/sciadv.abk0644
Schools: School of Materials Science and Engineering 
Rights: © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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