A β-VAE-GAN plus sensor-conditioned Transformer with Easy Attention forecasts near-wall turbulence in the Minimal Flow Unit, recovering 87% turbulent kinetic energy in 4D latent space and maintaining accuracy over 17288 t+ from 128 t+ initialization while reconstructing 82% TKE end-to-end.
On deep-learning-based closures for algebraic surrogate models of turbulent flows.Journal of Fluid Mechanics, 1020:A36, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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A Hybrid Generative Reduced-Order Model for the Minimal Flow Unit
A β-VAE-GAN plus sensor-conditioned Transformer with Easy Attention forecasts near-wall turbulence in the Minimal Flow Unit, recovering 87% turbulent kinetic energy in 4D latent space and maintaining accuracy over 17288 t+ from 128 t+ initialization while reconstructing 82% TKE end-to-end.
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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.