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Abstract
Machine learning (ML) is nowadays an important toolbox for theoretical and experimental physics, and its importance is expected to steadily grow in the coming years. Thanks to its effectiveness and extreme flexibility, it allows for applications covering a huge set of topics, ranging from statistical data analysis, to simulation and modeling. For this reason ML has been successfully used in very different research areas, such as high-energy physics, astrophysics and cosmology, condensed matter and statistical physics.
Applications in different domains often share strong similarities either in the problems to be solved or in the methodology employed. This motivates a fruitful exchange of ideas, which however is seldom achieved in practice due to the distance among different research communities.
The aim of the workshop is to bring together researchers with interests and expertise in ML from different fields in physics, strongly encouraging and promoting cross-topic exchange of ideas and collaborations. Three broad research areas will be covered:
- High-Energy Physics
- Astrophysics, Cosmology and Astroparticles
- Condensed Matter and Statistical Physics (including Quantum Information)
The distinctive trait of the workshop will be the focus on theoretical physics in a broad sense, including data analysis as well as simulation and modelling.
Topics
- Methods for regression and statistical analysis
- Monte Carlo integration and simulation
- Anomaly detection
- Classification
- Time series analysis
- Clustering and multi-dimensional visualization
- Equation solving
- Artificial intelligence-inspired and -augmented science
- Statistical physics algorithms for optimization and learning problems
- Quantum machine learning
Organizers
Massimo Brescia (INAF Napoli)
Filippo Caruso (U. Firenze)
S. George Djorgovski (Caltech)
Duccio Fanelli (U. Firenze)
Alessandro Marconi (U. Firenze)
Florian Marquardt (Max Planck Erlangen)
Giuliano Panico (U. Firenze)
Jesse Thaler (MIT)
Andrea Wulzer (CERN & U. Padova)
Local organizer
Giuliano Panico (U. Firenze)
Contact
giuliano.panico@unifi.it