VEGA is a derivative-free genetic algorithm that constructs Voronoi neighborhoods around retained elite candidates to balance exploitation and exploration in high-dimensional statistical optimization.
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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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Voronoi-Elitism Genetic Algorithm: A Generic Derivative-Free Routine With Theory and Implementation for Statistical Optimization
VEGA is a derivative-free genetic algorithm that constructs Voronoi neighborhoods around retained elite candidates to balance exploitation and exploration in high-dimensional statistical optimization.
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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.