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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.02283 |
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| _version_ | 1866911777973862400 |
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| author | Bengtsson, Fritjof Doglioni, Caterina Ekman, Per Alexander Gallén, Axel Jawahar, Pratik Orucevic-Alagic, Alma Santasmasas, Marta Camps Skidmore, Nicola Woolland, Oliver |
| author_facet | Bengtsson, Fritjof Doglioni, Caterina Ekman, Per Alexander Gallén, Axel Jawahar, Pratik Orucevic-Alagic, Alma Santasmasas, Marta Camps Skidmore, Nicola Woolland, Oliver |
| contents | Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle. We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02283 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Baler -- Machine Learning Based Compression of Scientific Data Bengtsson, Fritjof Doglioni, Caterina Ekman, Per Alexander Gallén, Axel Jawahar, Pratik Orucevic-Alagic, Alma Santasmasas, Marta Camps Skidmore, Nicola Woolland, Oliver Computational Physics High Energy Physics - Experiment Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle. We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data. |
| title | Baler -- Machine Learning Based Compression of Scientific Data |
| topic | Computational Physics High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2305.02283 |