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.
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative 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.
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
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A Neural Surrogate Approach for Simulating Natural Convection Problems
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.
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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.
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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.