A Fourier neural operator trained on Boussinesq-compressible simulation pairs corrects Boussinesq predictions for natural convection, achieving SSIM near unity and MSE reductions of one to three orders of magnitude.
Khorrami, Jaber R
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
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.
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
Mesh graph network generalizes von Mises stress prediction to unseen 2D plate geometries and loads, outperforming conventional ML models on held-out cases.
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.
-
LEIA: Learned Environment for Interactive Architected Materials
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
-
Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries
Mesh graph network generalizes von Mises stress prediction to unseen 2D plate geometries and loads, outperforming conventional ML models on held-out cases.