Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Sparse Data Challenge and Gray-Box Models
Whitepaper #370 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: basic research. Additional topics: planetary magnetospheres.
by Mikhail Sitnov, Matt Argall, Harry Arnold, Enrico Camporeale, Kevin Genestreti, Hantao Ji, Yi-Hsin Liu, Adam Michael, Andrei Runov, Anthony Sciola, Kareem Sorathia, Grant Stephens, Nikolai Tsyganenko, Drew Turner, Alexandr Ukhorskiy, Chih-Ping Wang, and Simon Wing
Published onJul 31, 2023
Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Sparse Data Challenge and Gray-Box Models
To meet the challenge of sparse data sampling in case of in-situ observations in Earth’s magnetosphere, we propose to use modern artificial intelligence tools. To use of the acquired data and empirical reconstructions in the first-principles models it is proposed to develop gray-box models that combine simulations (white-box models) and data-derived reconstructions (black-box models).
Creative Commons Attribution 4.0 International License (CC-BY 4.0)
