Servus05
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Initial Dataset commit
Browse files- .gitattributes +1 -0
- README.md +22 -0
- dataset/highJitter.hdf5 +3 -0
- dataset/lowJitter.hdf5 +3 -0
- dataset/medJitter.hdf5 +3 -0
- dataset/noJitter.hdf5 +3 -0
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README.md
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---
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license: mit
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---
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---
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license: mit
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---
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# Symmetric basis convolutions for learning lagrangian fluid mechanics (Published at ICLR 2024) - Test Case I
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This dataset contains the data for the first test case (1D compressible SPH) for the paper Symmetric basis convolutions for learning lagrangian fluid mechanics (Published at ICLR 2024).
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You can find the full paper [here](https://arxiv.org/abs/2403.16680).
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The source core repository is available [here](https://github.com/tum-pbs/SFBC/) and also contains information on the data generation
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For the other test case datasets look here:
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[Test Case I (compressible 1D)](https://huggingface.co/datasets/Wi-Re/SFBC_dataset_i)
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[Test Case II (wcsph 2D)](https://huggingface.co/datasets/Wi-Re/SFBC_dataset_ii)
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[Test Case III (isph 2D)](https://huggingface.co/datasets/Wi-Re/SFBC_dataset_iii)
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[Test Case IV (3D)](https://huggingface.co/datasets/Wi-Re/SFBC_dataset_iv)
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## File Layout
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The datasets are stored as hdf5 files with a single file per experiment. Within each file there is a set of configuration parameters and each frame of the simulation stored separately as a group. Each frame contains information for all fluid particles and all potentially relevant information. For the 2D test cases there is a pre-defined test/train split on a simulation level, wheras the 1D and 3D cases do not contain such a split.
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dataset/highJitter.hdf5
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version https://git-lfs.github.com/spec/v1
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oid sha256:233d05931b4e62bf44510da5f56972dd38a43482371d66c0ec688469e6e33381
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size 305838368
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dataset/lowJitter.hdf5
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version https://git-lfs.github.com/spec/v1
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oid sha256:6a91f3effb9d5236ecbada782fe4a58359cf1f85125cf6beb09b53448d1c70c9
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size 305641112
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dataset/medJitter.hdf5
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version https://git-lfs.github.com/spec/v1
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size 305838368
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dataset/noJitter.hdf5
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version https://git-lfs.github.com/spec/v1
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size 305637496
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