EAGER: ADAPT: AI-Enhanced Sampling for Lattice Field Theory and Beyond

This award will use Artificial Intelligence to greatly speed up theoretical calculations in elementary particle physics. Currently the detailed non-linear equations governing the interactions of elementary particles are not directly solvable, so a computational technique called Lattice Field Theory is used to approximately solve them at separate points in space (the lattice) and then combined to get an approximate result.

The precision which can be obtained is only limited by computational time. Currently the largest High-Performance Computers in the world are kept busy with Lattice Field Theory equation solving. This work will apply a new technique called Normalizing Flows to greatly reduce the required compute time for these calculations.

Normalizing flows are a class of deep generative models that can effectively model complex, high-dimensional distributions and have the potential to transform many areas of physics. These models are designed by composing many simple invertible neural network layers designed to efficiently compute the desired result. These flows can model the probability distribution of data from a real experiment and thus spend computational time only on the parts of the calculation that will dominate the required solution.

This award will develop these normalizing flows for lattice field theory. The work will also enhance public outreach in the New York Public School system and maintain an active Artificial Intelligence social media presence.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.