| Reference : Machine Learning in Space Weather and how to handle it |
| Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers | |||
| Physical, chemical, mathematical & earth Sciences : Mathematics Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics | |||
| http://hdl.handle.net/10993/54351 | |||
| Machine Learning in Space Weather and how to handle it | |
| English | |
Palmirotta, Guendalina  [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >] | |
| 6-Jul-2022 | |
| Machine Learning Seminar | |
| July 06 2022 | |
| University of Luxembourg | |
| online | |
| [en] Machine leaning (ML), an imposing but not necessarily new method, is living today its golden age by achieving unforeseen results in numerous industrial applications.
On the other side, Space Weather (SW), which describes changing environmental conditions in near-Earth space, is becoming more and more important to our society. But how can SW benefit from the ongoing ML revolution? In the last decade, many researchers, like E. Camporeale, showed that SW possesses all the ingredients often required for a successful ML application. Using this large and freely data set of in situ and remote observations collected over several decades of space missions, it is possible to forecast and nowcast solar activity and thus protect our increasing satellites constellations and us! In this talk, we will give a warm introduction to this field and point out a number of open challenges that we believe is worth to discuss and to undertake. | |
| Researchers ; Professionals ; Students | |
| http://hdl.handle.net/10993/54351 | |
| https://legato-team.eu/guendalina-palmirotta-machine-learning-in-space-weather-and-how-to-handle-it/ |
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