Baler -- Machine Learning Based Compression of Scientific Data

Bengtsson, F.; Doglioni, C.; Ekman, P.A.; Gallen, A.; Jawahar, P.; Orucevic-Alagic, A.; Camps Santasmasas, Marta; Skidmore, N.; Woodland, O.

Abstract

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.

Citation

Bengtsson, F., Doglioni, C., Ekman, P., Gallen, A., Jawahar, P., Orucevic-Alagic, A., …Woodland, O. Baler -- Machine Learning Based Compression of Scientific Data

• Working Paper Type: Working Paper
• Deposit Date: Mar 21, 2024
• Publicly Available Date: Mar 25, 2024
• Publisher URL: https://arxiv.org/abs/2305.02283