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

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,…

• NS
  National Science Foundation

  Award: 2004556
• UD
  U.S. Department of Energy
• NT
  Nanyang Technological University
• MU
  Multidisciplinary University Research Initiative

  Award: FA9550-20-1-0358
• AF
  Air Force Office of Scientific Research

  Awards: FA9550-, FA9550-20-1-0358, FA9550-20-1, FA9550-20, FA9550, 1-0358