Dynamical Systems and Machine Learning Approaches to Sun-Earth Relations
The dynamics of the Sun strongly affects the interplanetary and circumterrestrial environment, causing phenomena that have a great impact on anthropic activities. In the past, the response of the Earth’s magnetosphere-ionosphere system to the changes of the solar wind and interplanetary conditions due to the solar activity has been widely investigated, showing how the dynamics of the coupled solar wind-magnetosphere-ionosphere (SMI) system resembles that of a complex system, showing scale-invariant features, turbulence and a near-criticality behaviour. On the other hand, in the framework of dynamical systems, several new tools and methods have been proposed to quantify and characterise the dynamical complexity and its role in nonlinear out-of-equilibrium dynamical systems. Furthermore, the modelling of the complex dynamics of the SMI system such as some features of the solar activity has been shown to benefit from the recent advances in the field of machine learning techniques.
The course is devoted to young researchers and PhD students and will provide an introduction and an overview of the recent theoretical, numerical and data analysis advances in the framework of dynamical systems and machine learning approaches to the characterisation and the modelling of Sun-Earth’s relations. The course will consist of theoretical lectures and laboratory exercizes.