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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- cfd |
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- sciml |
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pretty_name: Shock-Induced Droplet Aerobreakup (SIDA) |
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size_categories: |
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- 10B<n<100B |
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--- |
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# Description: |
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This Dataset includes 128 trajectories of time-dependent Shock-Induced Droplet Aerobreakup (SIDA). Using a Finite |
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Volume solver, we solve the two-dimensional (2D) axisymmetric compressible Euler equations for this multiphase problem. |
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* dataset_name: Shock-induced Droplet Aero-breakup, |
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* PDE: 2D axisymmetric compressible Euler equations, |
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* created: 08-2025, |
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* time_dependent: true, |
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* include_initial_state: true, |
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# Spatiotemporal Information: |
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* num_trajectories: 128, |
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* num_time_steps: 61, |
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* num_channels: 6, |
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* channel_names: ["density", "pressure", "velocity_x", "velocity_y", "schlieren", "vorticity"], |
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* spatial_dimensions: 2, |
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* spatial_grid_size: [128, 256], |
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* dx=dy: 1.171875000e-05 |
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# Boundary conditions: |
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* west: Dirichlet, |
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* east: ZeroGradient, |
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* south: Axisymmetric, |
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* north: ZeroGradient |
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> Two separet files, "train.h5" and "test.h5", are provided. The former includes 96 trajectories for training and validation; the latter covers the 32 remaining trajectories for inference only. |
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> Refer to the "metadat_SIDA.json" file for more details on the dataset. |
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> A sample Out-of-Distribution, "OOD.h5", is added as well, which covers a higher shock Mach number across 32 trajectories. For more details, refer to "metadata_SIDA_OOD.json". |
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# Download: |
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The dataset can be downloaded, e.g., via `huggingface-cli download`. |
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```bash |
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huggingface-cli download FluidVerse/SIDA --repo-type dataset --local-dir . |
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``` |
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# Extra Data Generation: |
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Use the instructions inside the generation script, "generator.py", for creating larger datasets. This script runs the solver specified in the "metadata_SIDA.json" file. |
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# Strict Licensing Notice: |
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This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, or other commercial R&D applications—is strictly prohibited. |
