Machine learning discovers a tube-seeding strategy for IBP reduction of Feynman integrals that scales linearly with numerator power, demonstrated on rank-20 2-loop 5-point integrals.
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Kira 2.0 implements finite-field coefficient reconstruction for IBP reductions and improved user-equation handling, yielding lower memory use and faster performance on state-of-the-art problems.
LinApart3 performs multivariate partial fraction decomposition for linear-denominator rational functions using linear algebra and residue extraction on hyperplane arrangements, with guarantees on term structure, no spurious singularities, ordering independence, and spectator-variable insensitivity.
Contour equivalence in Feynman parameterization yields universal reduction formulas for one-loop integrals without integration-by-parts.
citing papers explorer
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Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
Machine learning discovers a tube-seeding strategy for IBP reduction of Feynman integrals that scales linearly with numerator power, demonstrated on rank-20 2-loop 5-point integrals.
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Integral Reduction with Kira 2.0 and Finite Field Methods
Kira 2.0 implements finite-field coefficient reconstruction for IBP reductions and improved user-equation handling, yielding lower memory use and faster performance on state-of-the-art problems.
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LinApart3: efficient algorithm for multivariate partial fraction decomposition with linear denominators
LinApart3 performs multivariate partial fraction decomposition for linear-denominator rational functions using linear algebra and residue extraction on hyperplane arrangements, with guarantees on term structure, no spurious singularities, ordering independence, and spectator-variable insensitivity.
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Feynman Integral Reduction without Integration-By-Parts
Contour equivalence in Feynman parameterization yields universal reduction formulas for one-loop integrals without integration-by-parts.