Refining IBP Reduction of Feynman Integrals with Machine Learning - Matthias Wilhelm - HeI seminar скачать в хорошем качестве

Refining IBP Reduction of Feynman Integrals with Machine Learning - Matthias Wilhelm - HeI seminar

A seminar for the HeI collaboration by Matthias Wilhelm from University of Southern Denmark with title: Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning. Abstract: In this talk, we will present recent progress on applying machine-learning techniques to improve 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 investigate the use of machine-learning techniques to find improved heuristics. We use funsearch, a genetic programming variant based on code generation by a Large Language Model, in order to explore possible approaches, then use strongly typed genetic programming to zero in on useful solutions. Both approaches manage to re-discover the state-of-the-art heuristics recently incorporated into integration-by-parts solvers, and in one example find a small advance on this state of the art.