In this research project, we aim at developing novel ways for understanding physical systems
consisting of many interacting constituents using techniques developed in the field of machine
learning. A common use of machine learning models is to classify pictures of different objects into
categories. Likewise, machine learning methods can be exploited to find hidden connections and
correlations between the different degrees of freedom in a physical system.
Computer resources required to apply standard algorithms to the study of complex physical
systems, typically scale with the system size in an exponential way. Overcoming this unfavorable
exponential scaling is a challenging issue. We will use neural networks as a model for these
physical systems and use the physics of the complex system to learn about the operation of the
neural networks. In this process the computer optimizes the different parameters in the neural
network such that it models the physical system under study. It is our aim to unravel the various
steps that the neural network has taken to model the physics. This will allow us to unravel the
underlying principles and to forge connections between machine learning and physics, to the
mutual benefit of both branches of sciences.