A one-step flow matching model using transformer in VAE latent space with non-Gaussian source and auxiliary networks generates accurate high-resolution path-dependent stress fields, achieving 6-7x CPU and ~100x GPU speedup over FEM.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields
A one-step flow matching model using transformer in VAE latent space with non-Gaussian source and auxiliary networks generates accurate high-resolution path-dependent stress fields, achieving 6-7x CPU and ~100x GPU speedup over FEM.
-
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.