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We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning…

Machine Learning · Computer Science 2018-03-26 Saul Toscano-Palmerin , Peter I. Frazier

Although multiview fusion has demonstrated potential in LiDAR segmentation, its dependence on computationally intensive point-based interactions, arising from the lack of fixed correspondences between views such as range view and Bird's-Eye…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Shoumeng Qiu , Xinrun Li , XiangYang Xue , Jian Pu

This paper introduces an efficient tensor-vector product technique for the rapid and accurate approximation of integral operators within physics-informed deep learning frameworks. Our approach leverages neural network architectures to…

Machine Learning · Computer Science 2024-09-04 Alireza Afzal Aghaei , Mahdi Movahedian Moghaddam , Kourosh Parand

In 1973, L.A. Levin published an algorithm that solves any inversion problem $\pi$ as quickly as the fastest algorithm $p^*$ computing a solution for $\pi$ in time bounded by $2^{l(p^*)}.t^*$, where $l(p^*)$ is the length of the binary…

Computational Complexity · Computer Science 2017-02-13 Fouad B. Chedid

PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonlinear dynamics (SINDy) (Brunton et al. 2016) approach to…

Within recent years, considerable progress has been made regarding high-performance solvers for Partial Differential Equations (PDEs), yielding potential gains in efficiency compared to industry standard tools. However, the latter largely…

Numerical Analysis · Mathematics 2024-02-20 Patrick Zimbrod , Michael Fleck , Johannes Schilp

Functions on a bounded domain in scientific computing are often approximated using piecewise polynomial approximations on meshes that adapt to the shape of the geometry. We study the problem of function approximation using splines on a…

Numerical Analysis · Mathematics 2020-08-27 Vincent Coppé , Daan Huybrechs

Zernike polynomials serve as an orthogonal basis on the unit disc, and have proven to be effective in optics simulations, astrophysics, and more recently in plasma simulations. Unlike Bessel functions, Zernike polynomials are inherently…

Performance · Computer Science 2025-11-25 Yigit Gunsur Elmacioglu , Rory Conlin , Daniel W. Dudt , Dario Panici , Egemen Kolemen

Feynman integral reduction based on intersection theory provides an alternative to the traditional integration-by-parts method, yet its practical application has been constrained by the large number of variables required in the computation.…

High Energy Physics - Theory · Physics 2026-04-08 Li-Hong Huang , Yan-Qing Ma , Ziwen Wang , Li Lin Yang

We introduce a method for computing some pseudo-elliptic integrals in terms of elementary functions. The method is simple and fast in comparison to the algebraic case of the Risch-Trager-Bronstein algorithm. This method can quickly solve…

Symbolic Computation · Computer Science 2020-09-25 Sam Blake

The Python package pylimer-tools is a comprehensive toolkit for computational studies of polymer networks, particularly bead-spring networks. The package provides functionality to generate polymer networks using Monte Carlo (MC) procedures…

Soft Condensed Matter · Physics 2025-08-18 Tim Bernhard , Fabian Schwarz , Andrei A. Gusev

Numerically estimating the integral of functions in high dimensional spaces is a non-trivial task. A oft-encountered example is the calculation of the marginal likelihood in Bayesian inference, in a context where a sampling algorithm such…

Data Analysis, Statistics and Probability · Physics 2020-03-30 Allen Caldwell , Philipp Eller , Vasyl Hafych , Rafael C. Schick , Oliver Schulz , Marco Szalay

Modern imaging methods rely strongly on Bayesian inference techniques to solve challenging imaging problems. Currently, the predominant Bayesian computation approach is convex optimisation, which scales very efficiently to high dimensional…

Computation · Statistics 2016-12-23 Alain Durmus , Eric Moulines , Marcelo Pereyra

When treating problems of vector diffraction in electromagnetic theory, the evaluation of the integral involving Bessel and associated Legendre functions is necessary. Here we present the analytical result for this integral that will make…

We present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as…

Cosmology and Nongalactic Astrophysics · Physics 2020-12-18 Vanessa Böhm , Yu Feng , Max E. Lee , Biwei Dai

Series involving hypergeometric functions are used to derive, extend and evaluate integrals involving the product of two Bessel functions of the first kind $J_{u}(a z)$ $J_{v}(b z)$ with order $u,v$, studied by Landau et al. The method used…

General Mathematics · Mathematics 2025-04-01 Robert Reynolds

We develop efficient numerical integration methods for computing an integral whose integrand is a product of a smooth function and the Gaussian function with a small standard deviation. Traditional numerical integration methods applied to…

Numerical Analysis · Mathematics 2018-04-12 Yunyun Ma , Yuesheng Xu

The moments of Bessel functions and Bessel-trigonometric functions play a basic role in many practical problems and numerical analysis. This paper presents a complete analysis for these moments based on the recursive relations of Bessel…

Numerical Analysis · Mathematics 2016-02-24 Yinkun Wang , Ying Li , Jianshu Luo

We propose an efficient and flexible method for solving Abel integral equation of the first kind, frequently appearing in many fields of astrophysics, physics, chemistry, and applied sciences. This equation represents an ill-posed problem,…

Instrumentation and Methods for Astrophysics · Physics 2016-08-26 I. I. Antokhin

TSIL is a library of utilities for the numerical calculation of dimensionally regularized two-loop self-energy integrals. A convenient basis for these functions is given by the integrals obtained at the end of O.V. Tarasov's recurrence…

High Energy Physics - Phenomenology · Physics 2016-05-18 Stephen P. Martin , David G. Robertson