arXiv:2606.10698v1 Announce Type: cross Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics. Our strategy allows us to reduce multi-loop integrals with large numerator powers via essentially the standard Laporta algorithm but with a sparse selection of seed integrals that grows only linearly with the numerator power, whereas existing strategies lead to growth with a polynomial power that i
The increasing sophistication of AI and machine learning techniques is enabling their application to traditionally complex and computationally intensive problems in theoretical physics, accelerating discovery.
This breakthrough provides a significant computational advantage for complex calculations in fundamental physics, potentially leading to faster progress in areas like particle theory and gravitational-wave physics.
The bottleneck in integrating Feynman integrals is substantially reduced, allowing for higher-precision calculations and tackling previously intractable problems in theoretical physics.
- · Theoretical particle physicists
- · Gravitational-wave physicists
- · AI/ML research in scientific domains
- · High-performance computing sector
- · Researchers without access to advanced AI tools
Computational efficiency for Feynman integrals improves significantly, enabling more complex calculations.
Faster validation or discovery of new fundamental particles or interactions, and more precise modeling of gravitational phenomena.
Enhanced theoretical understanding of the universe could guide experimental design, accelerating scientific progress in high-energy physics.
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Read at arXiv cs.LG