Create README.md

# Framing Neural Surrogates as Temporal Derivative Approximators
Datasets for Framing Neural Surrogates as Temporal Derivative Approximators. [(Paper)]()

Data is organized as:
```
- Split [train/valid]
- u : nodal values of the PDE solution, in shape [num_samples, temporal_resolution, spatial_resolution]
- x : coordinates of the spatial domain, in shape [spatial_resolution]
- t : timesteps of the PDE solution, in shape [temporal_resolution]
- coefficients [alpha, beta, gamma, etc.]: coefficients of the solved PDE solution, in shape [num_samples, coord_dim]
```

Data is generated for 5 equations: Advection, Heat, KS (1D); Burgers, NS (2D).

README.md

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+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - "train/Advection_4096.h5"
+    - "train/Heat_4096.h5"
+    - "train/KS_4096.h5"
+    - "train/Burgers_1024.h5"
+    - "train/NS_1024.h5"
+  - split: valid
+    path:
+    - "valid/Advection_256.h5"
+    - "valid/Heat_256.h5"
+    - "valid/KS_256.h5"
+    - "valid/Burgers_256.h5"
+    - "valid/NS_256.h5"
+---
+