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The Feynman-Kac equations are a type of partial differential equations describing the distribution of functionals of diffusive motion. The probability density function (PDF) of Brownian functionals satisfies the Feynman-Kac formula, being a…

Computational Physics · Physics 2015-02-03 Weihua Deng , Minghua Chen , Eli Barkai

The computational technique of $N$-fold Mellin-Barnes (MB) integrals, presented in a companion paper by the same authors, is used to derive sets of series representations of the massive one-loop conformal 3-point Feynman integral in various…

High Energy Physics - Theory · Physics 2021-05-19 B. Ananthanarayan , Sumit Banik , Samuel Friot , Shayan Ghosh

Physics-informed neural networks (PINNs) are a powerful approach for solving problems involving differential equations, yet they often struggle to solve problems with high frequency and/or multi-scale solutions. Finite basis…

Numerical Analysis · Mathematics 2024-06-21 Victorita Dolean , Alexander Heinlein , Siddhartha Mishra , Ben Moseley

Integration-by-parts reductions play a central role in perturbative QFT calculations. They allow the set of Feynman integrals contributing to a given observable to be reduced to a small set of basis integrals, and they moreover facilitate…

High Energy Physics - Theory · Physics 2016-07-08 Kasper J. Larsen , Yang Zhang

We show that the computational effort for the numerical solution of fermionic quantum systems, occurring e.g., in quantum chemistry, solid state physics, field theory in principle grows with less than the square of the particle number for…

Condensed Matter · Physics 2007-05-23 Peter Borrmann , Eberhard R. Hilf

Higher transcendental function occur frequently in the calculation of Feynman integrals in quantum field theory. Their expansion in a small parameter is a non-trivial task. We report on a computer program which allows the systematic…

Mathematical Physics · Physics 2008-11-26 Stefan Weinzierl

Highly oscillatory differential equations present significant challenges in numerical treatments. The Modulated Fourier Expansion (MFE), used as an ansatz, is a commonly employed tool as a numerical approximation method. In this article,…

Numerical Analysis · Mathematics 2024-07-17 Rafał Perczyński , Antoni Augustynowicz

New algebraic approach to analytical calculations of D-dimensional integrals for multi-loop Feynman diagrams is proposed. We show that the known analytical methods of evaluation of multi-loop Feynman integrals, such as integration by parts…

High Energy Physics - Theory · Physics 2010-04-05 A. P. Isaev

We study partial fraction decompositions (PFDs) in several variables using tools from commutative algebra. We give criteria for when a rational function with poles on a hyperplane arrangement has a desirable PFD. Our criteria are obtained…

Commutative Algebra · Mathematics 2026-03-25 Claire de Korte , Teresa Yu

We continue our study of the construction of analytical coefficients of the epsilon-expansion of hypergeometric functions and their connection with Feynman diagrams. In this paper, we apply the approach of obtaining iteratated solutions to…

High Energy Physics - Theory · Physics 2009-04-03 M. Yu. Kalmykov , B. F. L. Ward , S. A. Yost

The theory of renormalized energy spectrum of a multi-level quasiparticle interacting with polarization phonons at $T=0$ K is developed within the Feynman-Pines diagram technique in a new approach. It permits a successive separation of…

Strongly Correlated Electrons · Physics 2019-10-03 M. V. Tkach , Ju. O. Seti , O. M. Voitsekhivska , V. V. Gutiv

In this paper, we present a new approach to the construction of Mellin-Barnes representations for Feynman integrals inspired by the Method of Brackets. The novel technique is helpful to lower the dimensionality of Mellin-Barnes…

High Energy Physics - Phenomenology · Physics 2017-09-13 Mario Prausa

The evaluation of higher-loop Feynman integrals is at the core of the quest to reduce the uncertainty of theoretical predictions and match experimental data from the LHC and future colliders. pySecDec is a program to evaluate such integrals…

High Energy Physics - Phenomenology · Physics 2022-03-01 Vitaly Magerya

The calculation of two- and four-particle observables is addressed within the framework of the truncated polynomial expansion method (TPEM). The TPEM replaces the exact diagonalization of the one-electron sector in models for fermions…

Strongly Correlated Electrons · Physics 2009-11-11 G. Alvarez , T. C. Schulthess

We present a method to accelerate the numerical evaluation of spatial integrals of Feynman diagrams when expressed on the real frequency axis. This can be realized through use of a renormalized perturbation expansion with a constant but…

Strongly Correlated Electrons · Physics 2023-04-05 M. D. Burke , Maxence Grandadam , J. P. F. LeBlanc

Integration By Parts (IBP) is an important method for computing Feynman integrals. This work describes a formulation of the theory involving a set of differential equations in parameter space, and especially the definition and study of an…

High Energy Physics - Theory · Physics 2015-07-07 Barak Kol

In this manuscript, we elaborate on a procedure to derive $\epsilon$-factorised differential equations for multi-scale, multi-loop classes of Feynman integrals that evaluate to special functions beyond multiple polylogarithms. We…

High Energy Physics - Theory · Physics 2023-08-09 Lennard Görges , Christoph Nega , Lorenzo Tancredi , Fabian J. Wagner

We introduce the tools of intersection theory to the study of Feynman integrals, which allows for a new way of projecting integrals onto a basis. In order to illustrate this technique, we consider the Baikov representation of maximal cuts…

High Energy Physics - Theory · Physics 2019-03-06 Pierpaolo Mastrolia , Sebastian Mizera

This talk summarizes recent developments in the evaluation of Feynman integrals using hyperlogarithms. We discuss extensions of the original method, new results that were obtained with this approach and point out current problems and future…

High Energy Physics - Phenomenology · Physics 2014-07-02 Erik Panzer

Physics-informed neural networks (PINNs) [4, 10] are an approach for solving boundary value problems based on differential equations (PDEs). The key idea of PINNs is to use a neural network to approximate the solution to the PDE and to…

Numerical Analysis · Mathematics 2023-05-23 Victorita Dolean , Alexander Heinlein , Siddhartha Mishra , Ben Moseley