Machine learning methods open new perspectives on the high-dimensional data arising naturally in complex interacting systems, with applications ranging from analyzing experimental observations over optimal control to enhancing numerical simulations. On the other hand, methods from statistical physics and complex quantum many-body systems theory can improve the understanding of the working principles of modern machine learning methods. This SIG brings together experts to advance the field and identify promising directions for future research.

Members

• Sabine Andergassen
• Sebastian Erne
• Marcus Huber
• Stefan Rotter
• Jörg Schmiedmayer
• Alessandro Toschi
• Markus Wallerberger
• Vincenzo De Maio
• Ivona Brandic

External Members

• Moritz Helias
• Mario Krenn
• Igor Lesanovsky
• Florian Marquardt
• Georg Martius