Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density...
Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application
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Publisher
London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause...
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Full title
Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application
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TN_cdi_doaj_primary_oai_doaj_org_article_4977f9122559446caa9f43ba19f5e22a
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4977f9122559446caa9f43ba19f5e22a
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-022-11049-3
