Space Physics in the Context of Complex Systems Dynamics

Häftad, Engelska, 2027

1 677 kr

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Beskrivning

Space Physics in the Context of Complex Systems Dynamics explores the intricate interplay between space physics and complex systems science, emphasizing their methodologies and applications. The introduction establishes the significance of understanding space phenomena within the framework of complex systems. It presents a brief overview of machine learning, highlighting its relevance to the analysis of complex data in space physics. The text contrasts systems science with complex systems science, exploring various methodologies, including information theory approaches and causal inference techniques. Chapters illustrate how these methodologies can be utilized to enhance our understanding of complex dynamics in space systems. In its application section, the book addresses practical opportunities such as improving space weather forecasting, elucidating the relationship between magnetic storms and magnetospheric storms, and investigating the drivers behind radiation belt dynamics. Additionally, it discusses the potential for scientific discovery on other planets, showcasing the expansive implications of these methodologies. The conclusion outlines future perspectives, emphasizing the continued integration of information theory and machine learning to advance research in space physics. This book ultimately underscores the value of interdisciplinary approaches in tackling the complex challenges presented by space phenomena, encouraging a deeper exploration of the cosmos through the lens of complex systems. It highlights the necessity of collaborative methodologies to foster innovation and understanding in the rapidly evolving field of space physics.

Provides a comprehensive overview of complex systems techniques in the context of space physics, including information theory and causal inference
Emphasizes nonlinear and complex dynamics, offering insight into advanced analytical approaches beyond traditional linear correlation analysis
Integrates methodologies from complex systems science to provide a holistic understanding of space physics systems
Includes real-world applications such as space weather forecasting, magnetic storm-magnetospheric storm relationships, and drivers of radiation belt dynamics
Explores the synergy and integration possibilities between information theory and machine learning for enhanced data analysis and prediction in space sciences

Produktinformation

Utgivningsdatum:2027-03-01
Mått:152 x 229 x undefined mm
Format:Häftad
Språk:Engelska
Antal sidor:400
Förlag:Elsevier Science
ISBN:9780443416583

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Mer om författaren

Georgios Balasis is Research Director at the Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS) based at the National Observatory of Athens (NOA), Greece. Between 2002 and 2006, Dr. Balasis served as the specialist for global electromagnetic induction in the CHAMP satellite team at GeoForschungsZentrum Potsdam. Returning to Greece in 2006, he took on the role of an Assistant Researcher at the former Institute for Space Applications and Remote Sensing (ISARS) at NOA. In this capacity, Dr. Balasis was responsible for establishing, installing, and operating the HellENIc GeoMagnetic Array (ENIGMA) and facilitating its access to the SuperMAG worldwide collaboration of ground-based magnetometers, marking the pioneering operation of the first magnetometer station array in Greece. Dr. Balasis’s primary research interests encompass space physics, including the Sun-Earth connection and magnetosphere-ionosphere coupling, dynamics of the magnetosphere, space weather, and the geomagnetic field, as well as complex systems. Actively involved in magnetic satellite missions, Dr. Balasis serves as a member of the Science Advisory Group for the NanoMagSat Scout satellite mission by the European Space Agency, ESA Swarm satellite mission DISC (Data Innovation and Science Cluster) Advisory Board and ESA Swarm Data Quality Group and Validation Team. Dr. Balasis boasts an extensive publication record, featuring 1 book and over 85 refereed papers.Simon Wing has more than 20 years’ experience in space physics and space weather. He has authored and co-authoredover 100 papers and over 300 talks, and developed the Wing Kp Model that runs at several space weather centers aroundthe world. He also developed a technique for imaging plasma sheet ion properties from ionospheric observations. He iscurrently a Principal Staff Physicist at the Johns Hopkins University Applied Physics Laboratory. Reik Donner is Professor of Mathematics at the Potsdam Institute for Climate Impact Research, Germany, and a distinguished physicist with a strong background in mathematics and geoscience applications. He obtained his PhD in Physics from the University of Potsdam, Germany, in 2007. With extensive postdoctoral experience at institutions including Dresden University of Technology and the Max Planck Institutes for Physics of Complex Systems and Biogeochemistry, Dr. Donner's research has garnered international recognition, including a JSPS Postdoctoral Fellowship and a Guest Professorship at Osaka Prefecture University, Japan. In 2011, he received the EGU Division Outstanding Young Scientist Award for Nonlinear Processes. From 2014 to 2019, Dr. Donner led a research group focusing on Complex Systems Approaches for Understanding Causes and Consequences of Past, Present, and Future Climate Change at the Potsdam Institute for Climate Impact Research. He specializes in developing and applying statistical-dynamical methods to study climate variability, ecosystem dynamics, and socio-economic systems. Currently, as a professor at Magdeburg-Stendal University of Applied Sciences, Germany, Dr. Donner continues his groundbreaking research at the interface between complex systems theory and environmental data science. He has been actively involved in projects such as the EU ITN CAFE and the JPI Climate/JPI Oceans project ROADMAP, demonstrating his commitment to addressing pressing climate challenges.

Innehållsförteckning

1. Introduction2. Brief Machine Learning Intro3. Systems Science vs Complex Systems Science?Section 1 Methodology4. Information Theory Approaches (e.g. Balasis, Donner)5. Causal Inference Techniques (e.g. Palus, Runge, Wing)6. Complexity Science Methodologies7. System Science Methodologies8. Possibilities for Information Theory - Machine Learning Synergy/IntegrationSection 2 Applications9. Opportunities for space weather forecasting10. Magnetic storm-magnetospheric storm relationship11. Drivers of radiation belt dynamics12. Scientific discovery for other planets13. Conclusions and Future Perspectives