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Integral transformations are used to estimate high order derivatives of various special functions. Applications are given to numerical integration, where estimates of high order derivatives of the integrand are needed to achieve bounds on…

Numerical Analysis · Mathematics 2007-06-21 David M. Bradley

Kjolstad et. al. proposed a tensor algebra compiler. It takes expressions that define a tensor element-wise, such as $f_{ij}(a,b,c,d) = \exp\left[-\sum_{k=0}^4 \left((a_{ik}+b_{jk})^2\, c_{ii} + d_{i+k}^3 \right) \right]$, and generates the…

Symbolic Computation · Computer Science 2017-11-07 Sebastian Urban , Patrick van der Smagt

Algorithmic differentiation (AD) allows exact computation of derivatives given only an implementation of an objective function. Although many AD tools are available, a proper and efficient implementation of AD methods is not…

Mathematical Software · Computer Science 2018-07-27 Filip Šrajer , Zuzana Kukelova , Andrew Fitzgibbon

Transcendental Liouvillian extensions are differential fields, in which one can model poly-logarithmic, hyperexponential, and trigonometric functions, logarithmic integrals, and their (nested) rational expressions. For such an extension…

Symbolic Computation · Computer Science 2026-02-04 Shaoshi Chen , Hao Du , Yiman Gao , Hui huang , Wenqiao Li , Ziming Li

Computational methods for fractional differential equations exhibit essential instability. Even a minor modification of the coefficients or other entry data may switch good results to the divergent. The goal of this paper is to suggest the…

Numerical Analysis · Mathematics 2021-12-20 P. B. Dubovski , J. A. Slepoi

It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework. Noting that the versatility of modern automatic differentiation frameworks is…

Machine Learning · Statistics 2020-03-12 Fritz Obermeyer , Eli Bingham , Martin Jankowiak , Du Phan , Jonathan P. Chen

This paper presents a family of rapidly convergent summation formulas for various finite sums of analytic functions. These summation formulas are obtained by applying a series acceleration transformation involving Stirling numbers of the…

Number Theory · Mathematics 2016-02-02 Raphael Schumacher

Firedrake is a new tool for automating the numerical solution of partial differential equations. Firedrake adopts the domain-specific language for the finite element method of the FEniCS project, but with a pure Python runtime-only…

Fractional calculus generalizes the derivative and antiderivative operations of differential and integral calculus from integer orders to the entire complex plane. Methods are presented for using this generalized calculus with Laplace…

Classical Analysis and ODEs · Mathematics 2007-05-23 F. S. Felber

We present a new program package for calculating one-loop Feynman integrals, based on a new method avoiding Feynman parametrization and the contraction due to Passarino and Veltman. The package is calculating one-, two- and three-point…

High Energy Physics - Phenomenology · Physics 2007-05-23 Lars Brucher , Johannes Franzkowski

Many possible definitions have been proposed for fractional derivatives and integrals, starting from the classical Riemann-Liouville formula and its generalisations and modifying it by replacing the power function kernel with other kernel…

Classical Analysis and ODEs · Mathematics 2021-05-03 Arran Fernandez , Mehmet Ali Ozarslan , Dumitru Baleanu

In this paper, the notion of strongly typed language will be borrowed from the field of computer programming to introduce a calculational framework for linear algebra and tensor calculus for the purpose of detecting errors resulting from…

Differential Geometry · Mathematics 2012-12-12 Victor Dods

We present the library Collier for the numerical evaluation of one-loop scalar and tensor integrals in perturbative relativistic quantum field theories. The code provides numerical results for arbitrary tensor and scalar integrals for…

High Energy Physics - Phenomenology · Physics 2017-01-04 Ansgar Denner , Stefan Dittmaier , Lars Hofer

We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples (generalising the positional notation), projections of relations, and global and local…

Artificial Intelligence · Computer Science 2017-07-27 Alessandro Artale , Enrico Franconi , Rafael Peñaloza , Francesco Sportelli

Using the new variational approach proposed recently for a systematic improvement of the locally harmonic Feynman-Kleinert approximation to path integrals we calculate the partition function of the anharmonic oscillator for all temperatures…

High Energy Physics - Theory · Physics 2009-10-28 H. Kleinert , H. Meyer

Applying differentiable programming techniques and machine learning algorithms to foreign programs requires developers to either rewrite their code in a machine learning framework, or otherwise provide derivatives of the foreign code. This…

Mathematical Software · Computer Science 2022-08-05 William S. Moses , Valentin Churavy

Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these systems have been developed to compute derivatives of differentiable…

Machine Learning · Computer Science 2020-10-27 Wonyeol Lee , Hangyeol Yu , Xavier Rival , Hongseok Yang

We describe TensoriaCalc, a tensor calculus package written to be smoothly consistent with the Wolfram Language, so as to ensure ease of usage. It allows multiple metrics to be defined in a given session; and, once a metric is computed,…

General Relativity and Quantum Cosmology · Physics 2025-12-23 Wei-Hao Chen , Yi-Zen Chu , Vaidehi Varma

A method, recently advanced as the conformable Euler method, a general method for the finite difference discretization of fractional initial value problems for fractions in (0, 1], is shown to be valid only for the integer derivative. The…

General Mathematics · Mathematics 2021-09-24 D. P. Clemence-Mkhope

Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evaluating outlier detection results and effectively turn the…

Machine Learning · Computer Science 2023-09-04 Yu Wang , Lei Cao , Yizhou Yan , Samuel Madden