AI4Physics seminar: Neural Networks and Field Theory

Abstract: Machine learning techniques are leading to breakthroughs in the physical sciences and everyday life. The vast majority of the progress is powered by neural networks. In this colloquium I'll review the essentials of neural networks and we will quickly discover that their foundations make field theoretic language unavoidable. I will present two central results in ML theory regarding the statistics and dynamics of neural networks, their relation to free field theories, and the ML origin of interactions. I will then pivot and suggest using this formalism for physics, including what it would mean for a neural net ensemble to define a full-blown QFT. Time permitting, I will present progress in this direction, such as the realization of φ4 theory, unitarity, and conformal symmetry.

The seminar will be complemented by a tutorial on 22 May 15.15-16.30, 101136-A (Evelyn Sokolowski)

Title: Getting Your Hands Dirty with Neural Networks and Field Theory

Abstract: In this hands-on code-based tutorial, we will compute correlations in neural network field theory and demonstrate how interactions shut off in a large-N limit. Time permitting, we'll realize global symmetries and exemplify unitary NN-FTs on a lattice.