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Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale gracefully to large amounts of data and modern hardware for…

Machine Learning · Computer Science 2025-07-10 Jihao Andreas Lin

A practical and simple stable method for calculating Fourier integrals is proposed, effective both at low and at high frequencies. An approach based on the fruitful idea of Levin, to use of the collocation method to approximate the slowly…

Numerical Analysis · Mathematics 2021-04-09 Leonid A. Sevastianov , Konstantin P. Lovetskiy , Dmitry S. Kulyabov

A computational procedure is developed for the efficient calculation of derivatives of integrals over non-separable Gaussian-type basis functions, used for the evaluation of gradients of the total energy in quantum-mechanical simulations.…

Materials Science · Physics 2023-05-03 Jacques K. Desmarais , Alessandro De Frenza , Alessandro Erba

In this work we derive and analyze variational integrators of higher order for the structure-preserving simulation of mechanical systems. The construction is based on a space of polynomials together with Gauss and Lobatto quadrature rules…

Numerical Analysis · Mathematics 2014-04-08 Sina Ober-Blöbaum , Nils Saake

We study numerical computation of conformal invariants of domains in the complex plane. In particular, we provide an algorithm for computing the conformal capacity of a condenser. The algorithm applies for wide kind of geometries: domains…

Complex Variables · Mathematics 2020-08-19 Mohamed M S Nasser , Matti Vuorinen

Spatial numerical integration is essential for finite element analysis. Currently, numerical integration schemes, mostly based on Gauss quadrature, are widely used. Herein, we present an alternative semi-analytical approach for mass matrix…

Numerical Analysis · Mathematics 2015-06-09 Eli Hanukah

A general framework of Numerical Singular Integrals (NSI) method based on the Integration By Parts (IBP) has been developed for integrals involving singular and nearly singular integrands, or NSI-IBP. Through a general integration by parts…

Computational Engineering, Finance, and Science · Computer Science 2024-12-04 Shaolin Liao

The work reported in this article presents a high-order, stable, and efficient Gegenbauer pseudospectral method to solve numerically a wide variety of mathematical models. The proposed numerical scheme exploits the stability and the…

Numerical Analysis · Mathematics 2023-03-06 Kareem T. Elgindy

This article presents a novel approach to enhance the accuracy of classical quadrature rules by incorporating correction terms. The proposed method is particularly effective when the position of an isolated discontinuity in the function and…

Numerical Analysis · Mathematics 2025-01-27 Shipra Mahata , Samala Rathan , Juan Ruiz-Álvarez , Dionisio F. Yáñez

The Bayesian smoothing equations are generally intractable for systems described by nonlinear stochastic differential equations and discrete-time measurements. Gaussian approximations are a computationally efficient way to approximate the…

Dynamical Systems · Mathematics 2016-04-05 Juha Ala-Luhtala , Simo Särkkä , Robert Piché

We introduce a n-term quadrature to integrate inner products of n functions, as opposed to a Gaussian quadrature to integrate 2n functions. We will characterize and provide computational tools to construct the inner product quadrature, and…

Numerical Analysis · Mathematics 2012-05-04 Yu Chen

This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a single integral using the error function. This single…

Machine Learning · Statistics 2018-12-19 J. N. Hendriks , C. Jidling , A. Wills , T. B. Schön

Stable distributions are an important class of infinitely-divisible probability distributions, of which two special cases are the Cauchy distribution and the normal distribution. Aside from a few special cases, the density function for…

Numerical Analysis · Mathematics 2021-08-31 Sebastian Ament , Michael O'Neil

A fast multilevel algorithm based on directionally scaled tensor-product Gaussian kernels on structured sparse grids is proposed for interpolation of high-dimensional functions and for the numerical integration of high-dimensional…

Numerical Analysis · Mathematics 2015-01-15 Zhaonan Dong , Emmanuil H. Georgoulis , Jeremy Levesley , Fuat Usta

Multilevel Monte Carlo is a key tool for approximating integrals involving expensive scientific models. The idea is to use approximations of the integrand to construct an estimator with improved accuracy over classical Monte Carlo. We…

Methodology · Statistics 2023-03-15 Kaiyu Li , Daniel Giles , Toni Karvonen , Serge Guillas , François-Xavier Briol

Splitting methods constitute a widely used class of numerical integrators for ordinary and partial differential equations, particularly well suited to problems that can be decomposed into simpler subproblems. High-order splitting schemes…

Numerical Analysis · Mathematics 2026-04-02 Fernando Casas , Ander Murua

In this paper, we develop an efficient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical integration. Firstly, we briefly review the principles of…

Optics · Physics 2024-01-01 Wenfei Guo , Zizhe Cai , Zhongfei Xiong , Weijin Chen , Yuntian Chen

The numerical integration method has been routinely used to produce global standard gravitational models from satellite tracking measurements of CHAMP/GRACE types. It is implemented by solving the differential equations of the partial…

Geophysics · Physics 2019-09-26 Peiliang Xu

Among the single-trajectory Gaussian-based methods for solving the time-dependent Schr\"{o}dinger equation, the variational Gaussian approximation is the most accurate one. In contrast to Heller's original thawed Gaussian approximation, it…

Quantum Physics · Physics 2024-09-26 Roya Moghaddasi Fereidani , Jiří J. L. Vaníček

The specification of a covariance function is of paramount importance when employing Gaussian process models, but the requirement of positive definiteness severely limits those used in practice. Designing flexible stationary covariance…

Computation · Statistics 2024-05-01 Paul G. Beckman , Christopher J. Geoga
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