Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning

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@Article{Hippel:2025:JHEP,

• author = "Matt {von Hippel} and Matthias Wilhelm",
• title = "Refining Integration-by-Parts Reduction of {Feynman} Integrals with Machine Learning",
• journal = "Journal of High Energy Physics",
• year = "2025",
• pages = "article number 185",
• keywords = "genetic algorithms, genetic programming, STGP, AI, LLM, Scattering Amplitudes, Automation, Electroweak Precision Physics",
• ISSN = "1029-8479",
• URL = "https://arxiv.org/abs/2502.05121",
• URL = "https://link.springer.com/article/10.1007/JHEP05(2025)185",
• URL = "https://findresearcher.sdu.dk/ws/portalfiles/portal/290499844/JHEP05_2025_185_1_.pdf",
• DOI = "10.1007/JHEP05(2025)185",
• size = "26 pages",
• abstract = "Integration-by-parts reductions of Feynman integrals pose a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics, and rely on heuristic approaches for selecting integration-by-parts identities, whose quality heavily influences the performance. In this paper, 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.",
• notes = "JHEP Published for SISSA (International School for Advanced Studies) by Springer",

}

Genetic Programming entries for Matt von Hippel Matthias Oliver Wilhelm

Citations