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README.md
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license: cc-by-nc-4.0
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---
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---
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license: cc-by-nc-4.0
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---
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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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