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In this paper, we demonstrate that many of the computational tools for univariate orthogonal polynomials have analogues for a family of bivariate orthogonal polynomials on the triangle, including Clenshaw's algorithm and sparse…

Numerical Analysis · Mathematics 2019-02-14 Sheehan Olver , Alex Townsend , Geoff Vasil

Sparse spectral methods for solving partial differential equations have been derived in recent years using hierarchies of classical orthogonal polynomials on intervals, disks, and triangles. In this work we extend this methodology to a…

Numerical Analysis · Mathematics 2020-01-17 Ben Snowball , Sheehan Olver

Multivariate global polynomial approximations - such as polynomial chaos or stochastic collocation methods - are now in widespread use for sensitivity analysis and uncertainty quantification. The pseudospectral variety of these methods uses…

Numerical Analysis · Mathematics 2013-04-09 Paul G. Constantine , Michael S. Eldred , Eric T. Phipps

It is known that standard stochastic Galerkin methods face challenges when solving partial differential equations (PDEs) with random inputs. These challenges are typically attributed to the large number of required physical basis functions…

Numerical Analysis · Mathematics 2025-08-27 Guanjie Wang , Qifeng Liao

Novel fully discrete schemes are developed to numerically approximate a semilinear stochastic wave equation driven by additive space-time white noise. Spectral Galerkin method is proposed for the spatial discretization, and exponential time…

Numerical Analysis · Mathematics 2020-08-10 Xiaojie Wang , Siqing Gan , Jingtian Tang

We consider linear dynamical systems of ordinary differential equations or differential algebraic equations. Physical parameters are substituted by random variables for an uncertainty quantification. We expand the state variables as well as…

Numerical Analysis · Mathematics 2016-05-24 Roland Pulch

In the spectral Petrov-Galerkin methods, the trial and test functions are required to satisfy particular boundary conditions. By a suitable linear combination of orthogonal polynomials, a basis, that is called the modal basis, is obtained.…

Numerical Analysis · Mathematics 2018-02-14 Mostafa Jani , Esmail Babolian , Shahnam Javadi

A novel mixed spectral-Galerkin method based on generalized ball polynomials is proposed for solving the biharmonic equation on a unit ball. By introducing an auxiliary variable to decouple the biharmonic equation into a system of…

Numerical Analysis · Mathematics 2026-05-29 Mengxue Gao , Bing Su , Jianwei Zhou

In recent years, sparse spectral methods for solving partial differential equations have been derived using hierarchies of classical orthogonal polynomials on intervals, disks, disk-slices and triangles. In this work we extend the…

Numerical Analysis · Mathematics 2020-12-22 Ben Snowball , Sheehan Olver

The simulation of high-dimensional problems with manageable computational resource represents a long-standing challenge. In a series of our recent work [25, 17, 18, 24], a class of sparse grid DG methods has been formulated for solving…

Numerical Analysis · Mathematics 2019-06-27 Wei Guo

We apply polynomial approximation methods -- known in the numerical PDEs context as spectral methods -- to approximate the vector-valued function that satisfies a linear system of equations where the matrix and the right hand side depend on…

Numerical Analysis · Mathematics 2013-05-21 Paul G. Constantine , David F. Gleich , Gianluca Iaccarino

The generalized weak Galerkin (gWG) finite element method is proposed and analyzed for the biharmonic equation. A new generalized discrete weak second order partial derivative is introduced in the gWG scheme to allow arbitrary combinations…

Numerical Analysis · Mathematics 2023-02-14 Dan Li , Chunmei Wang , Junping Wang

We present an overarching framework for stable spectral methods on a triangle, defined by a multivariate W-system and based on orthogonal polynomials on the triangle. Motivated by the Koornwinder orthogonal polynomials on the triangle, we…

Numerical Analysis · Mathematics 2024-08-05 Jing Gao , Arieh Iserles

In this paper, a non-polynomial spectral Petrov-Galerkin method and associated collocation method for substantial fractional differential equations (FDEs) are proposed, analyzed, and tested. We extend a class of generalized Laguerre…

Numerical Analysis · Mathematics 2014-08-27 Can Huang , Qingshuo Song , Zhimin Zhang

We develop a sparse spectral method for a class of fractional differential equations, posed on $\mathbb{R}$, in one dimension. These equations can include sqrt-Laplacian, Hilbert, derivative and identity terms. The numerical method utilizes…

Numerical Analysis · Mathematics 2024-06-12 Ioannis P. A. Papadopoulos , Sheehan Olver

We introduce and analyse a sparse spectral method for the solution of Volterra integral equations using bivariate orthogonal polynomials on a triangle domain. The sparsity of the Volterra operator on a weighted Jacobi basis is used to…

Numerical Analysis · Mathematics 2024-09-23 Timon S. Gutleb , Sheehan Olver

In this study, we consider the numerical solution of large systems of linear equations obtained from the stochastic Galerkin formulation of stochastic partial differential equations. We propose an iterative algorithm that exploits the…

Numerical Analysis · Mathematics 2016-05-18 Kookjin Lee , Howard C. Elman

We present a fast spectral Galerkin scheme for the discretization of boundary integral equations arising from two-dimensional Helmholtz transmission problems in multi-layered periodic structures or gratings. Employing suitably parametrized…

Numerical Analysis · Mathematics 2020-11-06 José Pinto , Rubén Aylwin , Carlos Jerez-Hanckes

This paper presents a generalized weak Galerkin (gWG) finite element method for linear elasticity problems on general polygonal and polyhedral meshes. The proposed framework is flexible and efficient, allowing for the use of nonpolynomial…

Numerical Analysis · Mathematics 2026-01-27 Junping Wang , Yue Wang

The subject of this work is an adaptive stochastic Galerkin finite element method for parametric or random elliptic partial differential equations, which generates sparse product polynomial expansions with respect to the parametric…

Numerical Analysis · Mathematics 2025-03-28 Markus Bachmayr , Martin Eigel , Henrik Eisenmann , Igor Voulis
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