The paper trains a Transformer on Fourier spectral coefficients to surrogate 1D Burgers and 2D Navier-Stokes dynamics, but its claimed superiority over numerical and ML baselines is not demonstrated.
Acta Mechanica Sinica 38(11), 1725–1737 (2022)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction
The paper trains a Transformer on Fourier spectral coefficients to surrogate 1D Burgers and 2D Navier-Stokes dynamics, but its claimed superiority over numerical and ML baselines is not demonstrated.