International School of Space Science.
The International School of Space Science (ISSS) began its activity in 1991. The School is organized by the Consorzio Interuniversitario per la Fisica Spaziale (C.I.F.S.) which joins several Italian Universities operating in the field of Space Science and the Istituto Nazionale di Astrofisica (INAF). Since its foundation the ISSS has been directed by prof. U. Villante.
Dynamical Systems and Machine Learning Approaches to Sun-Earth Relations
15-19 June 2020, L’Aquila (Italy)
URGENT - COVID-19 ISSS June Course Rescheduled
Due to the recent situation caused by the Covid-19, the Course on “Dynamical Systems and Machine Learning Approaches to Sun-Earth Relations”, expected on 15-19 June, 2020, will be rescheduled to a later date.
All related information will be posted on the ISSS website.
The dynamics of the Sun strongly affects the interplanetary and circumterrestrial environment, causing phenomena that have a great impact on the 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 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 in theoretical lectures and laboratory