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Related papers: Shenfun -- automating the spectral Galerkin method

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We present a Python module named PyCheb, to solve the ordinary differential equations by using spectral collocation method. PyCheb incorporates discretization using Chebyshev points, barycentric interpolation and iterate methods. With this…

Mathematical Software · Computer Science 2016-11-07 Shaohui Liu , Tianshi Wang , Youran Zhang

As an extension of our previous work in Sun et.al (2018) [41], we develop a discontinuous Galerkin method for solving cross-diffusion systems with a formal gradient flow structure. These systems are associated with non-increasing entropy…

Numerical Analysis · Mathematics 2018-10-09 Zheng Sun , José Antonio Carrillo , Chi-Wang Shu

Solitons are ubiquitous in nature and play a pivotal role in the structure and dynamics of solutions of nonlinear propagation equations. In many instances where solitons exist, analytical expressions of these special objects are not…

Computational Physics · Physics 2025-04-16 O. Melchert , A. Demircan

An integro-differential equation, modeling dynamic fractional order viscoelasticity, with a Mittag-Leffler type convolution kernel is considered. A discontinuous Galerkin method, based on piecewise constant polynomials is formulated for…

Numerical Analysis · Mathematics 2015-01-20 Stig Larsson , Milena Racheva , Fardin Saedpanah

We present a family of Python modules for the numerical integration of ordinary, delay, or stochastic differential equations. The key features are that the user enters the derivative symbolically and it is just-in-time-compiled, allowing…

Mathematical Software · Computer Science 2018-04-25 Gerrit Ansmann

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

When the molecules of a gaseous system are far apart, say in microscale gas flows where the surface to volume ratio is high and hence the surface forces dominant, the molecule-surface interactions lead to the formation of a local…

Computational Physics · Physics 2019-07-02 Shashank Jaiswal , Jingwei Hu , Julien K. Brillon , Alina A. Alexeenko

In this paper, we construct an efficient numerical scheme for full-potential electronic structure calculations of periodic systems. In this scheme, the computational domain is decomposed into a set of atomic spheres and an interstitial…

Numerical Analysis · Mathematics 2024-12-20 Xiaoxu Li , Huajie Chen

Spacetime discontinuous Galerkin (SDG) finite element methods are used to solve such PDEs involving space and time variables arising from wave propagation phenomena in important applications in science and engineering. To support an…

Computational Geometry · Computer Science 2008-04-08 Shripad Thite

We present a mixed finite element solver for the linearized R13 equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial…

Computational Engineering, Finance, and Science · Computer Science 2021-04-27 Lambert Theisen , Manuel Torrilhon

We present a reduced basis stochastic Galerkin method for partial differential equations with random inputs. In this method, the reduced basis methodology is integrated into the stochastic Galerkin method, resulting in a significant…

Numerical Analysis · Mathematics 2023-10-02 Guanjie Wang , Qifeng Liao

In Trefftz discontinuous Galerkin methods a partial differential equation is discretized using discontinuous shape functions that are chosen to be elementwise in the kernel of the corresponding differential operator. We propose a new…

Numerical Analysis · Mathematics 2023-04-27 Christoph Lehrenfeld , Paul Stocker

In this paper, Bernstein piecewise polynomials are used to solve the integral equations numerically. A matrix formulation is given for a non-singular linear Fredholm Integral Equation by the technique of Galerkin method. In the Galerkin…

Numerical Analysis · Computer Science 2013-09-26 Afroza Shirin , Md. Shafiqul Islam

The goal of this paper is to present an overview of the software collection for the solution of linear and nonlinear semidefinite optimization problems PENNON. In the first part we present theoretical and practical details of the underlying…

Optimization and Control · Mathematics 2015-04-29 Michal Kocvara , Michael Stingl

We propose a new semi-analytic physics informed neural network (PINN) to solve singularly perturbed boundary value problems. The PINN is a scientific machine learning framework that offers a promising perspective for finding numerical…

Numerical Analysis · Mathematics 2022-08-22 Gung-Min Gie , Youngjoon Hong , Chang-Yeol Jung

In this paper we present an immersed weak Galerkin method for solving second-order elliptic interface problems on polygonal meshes, where the meshes do not need to be aligned with the interface. The discrete space consists of constants on…

Numerical Analysis · Mathematics 2022-08-17 Hyeokjoo Park , Do Y. Kwak

Local discontinuous Galerkin methods are developed for solving second order and fourth order time-dependent partial differential equations defined on static 2D manifolds. These schemes are second-order accurate with surfaces triangulized by…

Numerical Analysis · Mathematics 2024-01-05 Shixin Xu , Zhiliang Xu

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

A novel discontinuous Galerkin (DG) method is developed to solve time-dependent bi-harmonic type equations involving fourth derivatives in one and multiple space dimensions. We present the spatial DG discretization based on a mixed…

Numerical Analysis · Mathematics 2019-10-02 Hailiang Liu , Peimeng Yin

Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning. However, conventional PINNs perform pointwise predictions that neglect dependencies within a domain, which may result in suboptimal…

Machine Learning · Computer Science 2025-05-26 Mayank Nagda , Phil Ostheimer , Thomas Specht , Frank Rhein , Fabian Jirasek , Stephan Mandt , Marius Kloft , Sophie Fellenz