Advanced mathematics and machine learning to exploit the value of data for climate policies

A successful new course of the Future Earth Research School on “Data Science and Machine Learning for Climate Research” has recently concluded in Bertinoro (FC): 23 participants from 13 different countries gained new knowledge on the mathematical basis for the analysis of complex systems, such as the climate, through machine learning.

New applications of machine learning to climate models have the potential to reduce the time frame in which new research is produced and contribute to increasingly more accurate and detailed representations of Future Earth’s scenarios. 

During the third course of the Future Earth Research School (FERS) on “Data Science and Machine Learning for Climate Research”, held in Bertinoro (FC), Italy, from June 5 to 17, a diverse student community had the chance to dive into new machine learning methods and applications. With its now consolidated participatory teaching approach, the course featured lectures, seminars, hands-on sessions, and group projects. Participants acquired an operative knowledge of different modelling frameworks and data-driven methods for analysing large data sets. 

The course was held by an outstanding faculty composed by Stefan Klus, associate professor in mathematics at the Heriot-Watt University, Edinburgh; Houman Owhadi, professor of applied and computational mathematics and control and dynamical systems at the California Institute of Technology; Feliks Nüske, research group leader at the Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; Jürgen Kurths, professor of nonlinear dynamics at the Humboldt University, Berlin, and the chair of the Research Domain Complexity Science of the Potsdam Institute for Climate Impact Research; Sarah Wolf, leader of a MATH+ junior research group on “mathematics for sustainability transitions” at Freie Universität Berlin and Dimitrios Giannakis, professor of Mathematics at Dartmouth College. With the support of Cristiano De Nobili, lead AI scientist at Pi School.

Participants joined from 5 different continents and with a wide array of academic and professional backgrounds ranging from hard to applied sciences, as well as natural resource management, engineering, and computer sciences. The young professionals were trained to understand and interpret the underlying processes and relationships between different observations with a growing amount of collected and available data.   

Coordinated by the CMCC Foundation and funded by the Emilia-Romagna Region, FERS provides high-level scientific courses that give researchers the tools to understand and anticipate future global environmental challenges. A new FERS School on “Sea Level Rise and Coastal Adaptation” will be held from October 9 to 20, 2023. More information and the call for applications are published on the FERS website.

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