Exploring the Earth's Ionosphere with a Dynamical Systems Approach
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Upper Atmosphere".
Deadline for manuscript submissions: 1 August 2025 | Viewed by 26
Special Issue Editor
Interests: ionospheric physics; space weather; ecological models; fundaments of dissipation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modelling the Earth’s ionosphere as a whole, or regions of it, is a well-established branch of applied physics in which space physics and geophysics intersect. In this field, many lines of research co-exist; for example, empiric models co-exist with first principle models, and multi-instrument inversion techniques may aid in the assimilation of real data in theoretical modelling. Moreover, the growing use of machine learning and artificial intelligence techniques in this field offers unprecedented opportunities to exploit the large datasets associated with aeronomy and near-Earth space physics in order to create new empirical and hybrid models.
Despite the extensive application of machine learning in ionospheric modelling, it is crucial to maintain the theoretical modelling of the ionosphere, or of parts of it, as these models are dynamical systems that can benefit from recent developments in computer science but cannot avoid relying on the physical interpretation of each step they take.
This Special Issue of Atmosphere aims to collect research that represents the Earth’s ionosphere as a dynamical system that can be delineated using first principles, as deduced from a collection of empirical data and observations, or may be designed specifically for a particular phenomenon or set of phenomena.
The scope of this Special Issue includes, but is not limited to, the following topics:
- chaos theory,
- complex systems,
- artificial intelligence, machine learning and neural networks,
- stochastic dynamics,
- big data assimilation,
- first principle theory,
- modeling.
Dr. Massimo Materassi
Guest Editor
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Keywords
- chaos theory
- complex systems
- artificial intelligence
- machine learning and neural networks
- stochastic dynamics
- big data assimilation
- first Principle theory
- modeling
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