Strojové učení v kosmické fyzice
| Název práce v češtině: | Strojové učení v kosmické fyzice |
|---|---|
| Název v anglickém jazyce: | Machine learning in space physics |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | disertační práce |
| Jazyk práce: | čeština |
| Ústav: | Katedra fyziky povrchů a plazmatu (32-KFPP) |
| Vedoucí / školitel: | prof. RNDr. František Němec, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 28.07.2021 |
| Datum zadání: | 28.07.2021 |
| Datum potvrzení stud. oddělením: | 15.09.2021 |
| Konzultanti: | Gilbert Pi, Ph.D. |
| Zásady pro vypracování |
| The number of spacecraft and the amount of available data relevant for space physics studies increase tremendously in recent years. Automated ways of extraction of useful scientific information from big data volumes thus become a question of the utmost importance, which is the reason why machine learning algorithms are becoming more and more popular in the space physics field.
The aim of the thesis is to use machine learning techniques (neural networks, in particular) to analyze selected spacecraft data sets. A special focus will be on modeling significant plasma boundaries in space (bow shock, magnetopause, plasmapause). These are readily identifiable in the data as sudden specific changes of measured plasma parameters (density, flow velocity, magnetic field), and they are typically described by empirical models following prescribed shape/distance formulas. Machine learning techniques should allow not only to automatically identify the respective plasma regions and boundary crossings, but also to model the boundary locations themselves without the need for additional a priori assumptions. The evaluation of parameters controlling the shape and location of the derived model boundaries will then allow to improve our understanding of factors controlling the boundaries. |
| Seznam odborné literatury |
| 1) M. G. Kivelson, C. T. Russell: Introduction to Space Physics. University Press, Cambridge, 1995.
2) E. Camporeale, J. Johnson, S. Wing: Machine Learning Techniques for Space Weather. Elsevier, Radarweg, 2018. 3) Q. Zong, P. Escoubet, D. Sibeck, G. Le, H. Zhang: Dayside Magnetosphere Interactions. AGU, Geophysical Monograph Series, 2020. 4) Papers in scientific journals recommended by the thesis supervisor. |
| Předběžná náplň práce v anglickém jazyce |
| The number of spacecraft and the amount of available data relevant for space physics studies increase tremendously in recent years. Automated ways of extraction of useful scientific information from big data volumes thus become a question of the utmost importance, which is the reason why machine learning algorithms are becoming more and more popular in the space physics field.
The aim of the thesis is to use machine learning techniques (neural networks, in particular) to analyze selected spacecraft data sets. A special focus will be on modeling significant plasma boundaries in space (bow shock, magnetopause, plasmapause). These are readily identifiable in the data as sudden specific changes of measured plasma parameters (density, flow velocity, magnetic field), and they are typically described by empirical models following prescribed shape/distance formulas. Machine learning techniques should allow not only to automatically identify the respective plasma regions and boundary crossings, but also to model the boundary locations themselves without the need for additional a priori assumptions. The evaluation of parameters controlling the shape and location of the derived model boundaries will then allow to improve our understanding of factors controlling the boundaries. |