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| license: cc-by-nc-4.0 |
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| # SURF: A Generalisation Benchmark for GNNs Predicting Fluid Dynamics |
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| SURF, is a benchmark designed to test the generalization of learned graph-based fluid simulators. The benchmark consists of seven independent datasets: |
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| - Base |
| - Turned |
| - Topo |
| - Range |
| - Dynamic |
| - Full |
| - FullFiner |
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| Each dataset is available as separate *.zip file and consists of at least 1200 2D incompressible fluid flow simulations with 300 timesteps. |
| The data structure is as follows: |
| - folder: dataset_name |
| - folders: dpx |
| - files: sim.npz, triangles.py, constrained_kmeans_20.npy, Simulation_dp1_Timestep_50.png |
| - folder: Splits |
| - files: train.txt, test.txt, valid.txt |
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| The file sim.npz (numpy archive) contains the result of the simulation for each timestep at each node: |
| - 'pointcloud': x, y coordinates |
| - 'VX': velocity in x-direction |
| - 'VY': velocity in y-direction |
| - 'PS': static pressure |
| - 'PG': dynamic pressure |
| - 'T': temperature |
| - 'TC': thermal conductivity of fluid |
| - 'HC': heat capacity of fluid |
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| The results have the following shape: VX.shape=(#timesteps, #nodes, 1). |
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| The file triangles.py contains the mesh connectivity. triangles.shape=(#timesteps, #elements, 3). Each triangle is defined by the node numbers in counter clockwise direction. |
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