A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration

Key points

• Empirical formulae and scalar-output surrogates serve design screening, and mesoscale finite element models resolve the aggregate-scale response, but one solution cannot cover random aggregate samples at scale, and no framework links mesoscale heterogeneity to field-level prediction.1
• A physics-coupled graph neural operator (PC-GNO) is proposed, which learns in function space the evolution of the mid-plane six-component stress tensor field.1
• The coupling strength of message passing is constructed from the impedance contrast across the material interface, and the update is constrained by spectral normalization.1
• For field-level response prediction in concrete penetration, this result provides a data-driven route, with conditions of applicability defined by the training and the evaluation.1

Sources (1)

• [1]A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:45 PM

  The penetration-resistant design of concrete structures requires the spatiotemporal evolution of the stress field within the target, whereas in design only the strength grade, the geometry and the projectile parameters are known, and the aggregate arrangement is not.

  Empirical formulae and scalar-output surrogates serve design screening, and mesoscale finite element models resolve the aggregate-scale response, but one solution cannot cover random aggregate samples at scale, and no framework links mesoscale heterogeneity to field-level prediction.

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