Dataset of a parameterized U-bend flow for Deep Learning Applications
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Date
2023-03-09Author
Decke, Jens
Contributing Person/Institution
ProjectLeader: Wünsch, Olaf Prof. Dr.-Ing.
ProjectLeader: Sick, Bernhard Prof. Dr. rer. nat.
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This dataset contains 10,000 U-bend shapes. Each of them is described by 28 design parameters, which are
processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive
benchmark for investigating various problems and methods from the field of design optimization. For these
investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One
unique feature of this dataset is that each shape can be represented by three distinct data types including
design parameter and objective combinations. Five different resolutions of 2D images from the geometry and
the solution variables of the numerical simulation. As well as a representation using the cell values of the
numerical mesh. This third representation enables considering the specific data structure of numerical
simulations for deep learning approaches. The source code and the container which was used to generate the
data can be found in the git and docker repositorys mentioned below.
IMPORTANT: In case you use the data please cite our corresponding article mentioned below 1.0
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AndereRelated Resources
IsSupplementTo: (arXiv) TODO Link einfügen sobald vorhanden!IsSupplementedBy: (URL) https://git.ies.uni-kassel.de/jdecke/dataset-of-a-parameterized-u-bend-flow-for-deep-learning-applications
IsSupplementedBy: (URL) https://hub.docker.com/r/jdecke/foamy
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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0