Andrés Asensio Ramos, Mark C. M. Cheung, Iulia Chifu, Ricardo Gafeira
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引用次数: 1
Abstract
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.
期刊介绍：
Living Reviews in Solar Physics is a peer-reviewed, full open access, and exclusively online journal, publishing freely available reviews of research in all areas of solar and heliospheric physics. Articles are solicited from leading authorities and are directed towards the scientific community at or above the graduate-student level. The articles in Living Reviews provide critical reviews of the current state of research in the fields they cover. They evaluate existing work, place it in a meaningful context, and suggest areas where more work and new results are needed. Articles also offer annotated insights into the key literature and describe other available resources. Living Reviews is unique in maintaining a suite of high-quality reviews, which are kept up-to-date by the authors. This is the meaning of the word "living" in the journal''s title.
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