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PDE Forecasting on Kolmogorov Flow ν=1e-4

0.0007Wasserstein DistanceSI
-0.0060490.0393710.084790.130209Jun 30, 2026

Evaluation results

MethodLinks
SI2026.060.0007
PDE-TFrollout=one step ahead2026.060.0008
UNet2026.060.0012
PDE-Ttraining=MSE2026.060.0127
UNet Mmethod=Monte Carlo dropout2026.060.0809
ResNet2026.060.163
UNet Ploss=heteroscedastic Gaussian negative log likelihood2026.060.1689