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temporal_pdes

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ayz2 commited on Dec 16, 2024

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@@ -44,7 +44,7 @@ These can be downsampled to produce samples with varying timescales $\Delta t$.
   - 4096/256 samples, each sample of shape [400, 100] (num_timesteps, num_grid_points)
   - Viscosity $\nu = 1$ is constant
-### 12D 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).
 - Burgers
   - 1024/256 samples, each sample of shape [100, 64, 64] (num_timesteps, num_grid_x, num_grid_y)

   - 4096/256 samples, each sample of shape [400, 100] (num_timesteps, num_grid_points)
   - 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).
 - Burgers
   - 1024/256 samples, each sample of shape [100, 64, 64] (num_timesteps, num_grid_x, num_grid_y)