Machine Learning Techniques For Space Weather, 1St Edition
Language: English
Published by Elsevier, 2018
- Softcover
- New
Seller: Biblios, frankfurt am main, hessen, GermanyBiblios
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- Title
- Machine Learning Techniques For Space Weather, 1St Edition
- Publisher
- Elsevier
- Publication year
- 2018
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0128117885
- ISBN 13
- 9780128117880
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms.
Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields.
- Collects many representative non-traditional approaches to space weather into a single volume
- Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists
- Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms
"Synopsis" may belong to another edition of this title.
About the Author
Simon Wing has more than 20 years’ experience in space physics and space weather. He has authored and co-authored
over 100 papers and over 300 talks, and developed the Wing Kp Model that runs at several space weather centers around
the world. He also developed a technique for imaging plasma sheet ion properties from ionospheric observations. He is
currently a Principal Staff Physicist at the Johns Hopkins University Applied Physics Laboratory.
Jay Johnson earned his Ph.D. degree in physics from the Massachusetts Institute of Technology and has held research positions at University of Alaska, Massachusetts Institute of Technology, and Princeton Plasma Physics Laboratory, where he served as Principal Research Physicist, co-director of the Princeton Center for Heliophysics, and Head of Space Physics from 2005-2016. He is currently a professor in the Department of Engineering and Computer Science at Andrews University, Michigan. He has published over 70 papers on theoretical plasma physics with emphasis on applications to space plasmas.
"About the title" may belong to another edition of this title.
Biblios
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