Update README.md

README.md

@@ -45,7 +45,7 @@ These can be downsampled to produce samples with varying timescales \\(\Delta t\
   - Viscosity \\(\nu = 1\\) is constant
 
 ### 2D PDEs
-These can be downsampled to produce samples with varying timescales \\(\Delta t\\). Burgers data are generated from [Masked Autoencoder are PDE Learners](https://github.com/anthonyzhou-1/mae-pdes), and NS data are generated from Fourier Neural Operator for Parametric Partial Differential Equations (repo no longer exists).
+These can be downsampled to produce samples with varying timescales \\(\Delta t\\). Burgers data are generated from [Masked Autoencoder are PDE Learners](https://github.com/anthonyzhou-1/mae-pdes), and NS data are generated from Fourier Neural Operator for Parametric Partial Differential Equations (repo no longer exists). Kolmogorov Flow data is from [APEBench](https://github.com/tum-pbs/apebench)
 - Burgers
   - 1024/256 samples, each sample of shape [100, 64, 64] (num_timesteps, num_grid_x, num_grid_y)
   - \\(c_x, c_y\\) is uniformly sampled from [0.5, 1.0] and \\(\nu\\) is uniformly sampled from [7.5e-3, 1.5e-2]