A GNN-LSTM surrogate trained on Voronoi-cell homogenized nonlinear FE data predicts unseen SFT microstructure responses with R²≈0.98 and >100x speedup over direct FE.
Torquato, Random Heterogeneous Materials: Microstructure and Macroscopic Properties, V ol
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Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.
citing papers explorer
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On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks
A GNN-LSTM surrogate trained on Voronoi-cell homogenized nonlinear FE data predicts unseen SFT microstructure responses with R²≈0.98 and >100x speedup over direct FE.
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Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.