Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050

2020/09/11 by Ryan McGranaghan, McGranaghan, R. M., B. Thompson +26
Decision Sciences · Environmental Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Atmospheric and Environmental Gas Dynamics #Big Data Technologies and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Solar and Space Plasma Dynamics #Solar and Stellar Astrophysics (astro-ph.SR)

paper · doi:10.48550/arxiv.2212.13325

openalex publication_date 2020/09/11 · openalex created_date 2023/01/06 · openalex updated_date 2026/07/28

Abstract

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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