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.