INTRO

The field of machine learning and artificial intelligence has flourished over the past years, fueled by a new generation of algorithms, ever growing resources of computational power, and the availability of “big data” in many fields. These technological advances have a strong impact also on the natural sciences, motivating researchers across all fields to explore machine learning techniques as an extension of their numerical toolbox.

This summer school will cover applications of machine learning (ML) approaches in condensed matter physics including their relation to established techniques. In fact, dealing with the high dimensional and complex data that naturally arise in many-body physics seems to call exactly for the proven strengths of ML techniques — pattern recognition and dimensional reduction. The school will illuminate how ML approaches can help us to identify and analyze phase transitions based on microscopic data. Furthermore, we will see how deep learning can help us tame the “curse of dimensionality” that is inherent to quantum many-body systems by representing physical quantum states as artificial neural networks. Finally, we will sound out connections between ML and tensor networks, which have proven their remarkably versatile utility over the past decades in condensed matter physics.

ORGANIZERS: Augustine Kshetrimayum (Freie Universität Berlin) | Daniel Kaplan (Weizmann Institute) | Markus Schmitt (Universität zu Köln) | Sukhbinder Singh (Monash University, Australia)

Contact: crcsschool2021@gmail.com

Video recordings of the presentations: