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.
A supervised neural network for drag prediction of arbitrary 2d shapes in laminar flows at low reynolds number.Computers & Fluids, 210:104645, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
Deep neural networks are trained to recover low-order Fourier elliptical components describing overall shape and orientation from simulated transit light curves of arbitrary 2D objects.
citing papers explorer
-
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.