Work

SpacePhyML: Enabling Access to Space Physics Data for Machine Learning Applications

Year: 2025

Type: article

Abstract: Presented at Machine Learning and Computer Vision in Heliophysics 2025

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17152370

Authors: Ekelund, Jonah, Raptis, Savvas, Markidis, Stefano

Institutions: KTH Royal Institute of Technology, Johns Hopkins University Applied Physics Laboratory

Language: English

Topic: Solar and Space Plasma Dynamics

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Automatics in Space Exploration

OpenAlex ID: W7083056947

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