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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 |
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- Turned |
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- Topo |
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- Range |
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- Dynamic |
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- Full |
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- 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. |
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The data structure is as follows: |
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- folder: dataset_name |
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- folders: dpx |
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- files: sim.npz, triangles.py, constrained_kmeans_20.npy, Simulation_dp1_Timestep_50.png |
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- folder: Splits |
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- 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: |
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- 'pointcloud': x, y coordinates |
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- 'VX': velocity in x-direction |
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- 'VY': velocity in y-direction |
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- 'PS': static pressure |
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- 'PG': dynamic pressure |
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- 'T': temperature |
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- 'TC': thermal conductivity of fluid |
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- '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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