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- Title:
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
- Document type:
- Forschungsdaten
- Publication date:
- 29.03.2023
- Responsible:
- Kohl, Georg
- Authors:
- Kohl, Georg; Chen, Li-Wei; Thuerey, Nils
- Author affiliation:
- TUM
- Publisher:
- TUM
- Identifier:
- doi:10.14459/2023mp1703144
- End date of data production:
- 01.02.2023
- Subject area:
- DAT Datenverarbeitung, Informatik; PHY Physik
- Resource type:
- Simulationen / simulations
- Data type:
- Datenbanken / data bases
- Other data type:
- Compressed numpy arrays of dense volumetric fields
- Description:
- This archive contains volumetric data from different PDE simulations. It can be used to train and evaluate the similarity assessment of different metrics.
- Method of data assessment:
- This data set also contains a selection of data from the Johns Hopkins Turbulence Database
( http://turbulence.pha.jhu.edu) as detailed in our source code. - Links:
Project: https://ge.in.tum.de/publications/2022-volsim-kohl
Publication: https://arxiv.org/abs/2202.04109
Source Code: https://github.com/tum-pbs/VOLSIM- Key words:
- Deep Learning; Similarity Assessment; Fluid Simulation
- Technical remarks:
- View and download (89 GB total, 16 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1703144):
rsync rsync://m1703144@dataserv.ub.tum.de/m1703144/ - Language:
- en
- Rights:
- by, http://creativecommons.org/licenses/by/4.0
- Horizon 2020:
- ERC 2019 CoG 863850 (SpaTe)
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