Hybrid GNN-FEM surrogate replaces only the phase-field update step in a staggered incremental scheme for phase-field fracture, using dimensionless mesh features and physics-informed loss to generalize across geometries, loads, materials, and discretizations.
A., Brummund, J., & Kästner, M
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
Sequential subspace adaptation plus E3C hyper-reduction yields a lower-dimensional affine subspace for dissipative microstructure homogenization while preserving a projected Hill-Mandel condition.
citing papers explorer
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A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling
Hybrid GNN-FEM surrogate replaces only the phase-field update step in a staggered incremental scheme for phase-field fracture, using dimensionless mesh features and physics-informed loss to generalize across geometries, loads, materials, and discretizations.
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Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
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Sequential Subspace Mode Adaptation for the Reduced-Order Homogenization of Dissipative Microstructures using E3C Hyper-Reduction
Sequential subspace adaptation plus E3C hyper-reduction yields a lower-dimensional affine subspace for dissipative microstructure homogenization while preserving a projected Hill-Mandel condition.