Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050
Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discov...
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| Veröffentlicht in: | arXiv.org 2022-12 |
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| creator | McGranaghan, R M Thompson, B Camporeale, E Bortnik, J Bobra, M Lapenta, G Wing, S Poduval, B Lotz, S Murray, S Kirk, M Chen, T Y Bain, H M Riley, P Tremblay, B Cheung, M Delouille, V |
| description | Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge. |
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| subjects | Data science Knowledge representation Machine learning |
| title | Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050 |
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