Machine learning methods open new perspectives on the high-dimensional data arising naturally in complex interacting systems, with applications ranging from the analysis of experimental observations over optimal control to the enhancement of numerical simulations. On the other hand, methods from statistical physics and complex (quantum) many-body systems theory can be used to better understand 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
• Joerg Schmiedmayer
• Alessandro Toschi
• Markus Wallerberger