Speaker
Description
In this talk, we will present recent progress on applying machine-learning techniques to speed up calculations in theoretical physics, in which we desire exact and analytic results. One example are so-called integration-by-parts reductions of Feynman integrals, which pose a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics. These reductions rely on heuristic approaches for selecting a finite set of linear equations to solve, and the quality of the heuristics heavily influences the performance. In this talk, we present how a variety of machine-learning techniques (RL, evolutionary strategies and language agents) can identify improved heuristics that speed up the reductions by several orders of magnitude.