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Related papers: Explicit radial basis function Runge-Kutta methods

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A time discretization method is called strongly stable, if the norm of its numerical solution is nonincreasing. It is known that, even for linear semi-negative problems, many explicit Runge--Kutta (RK) methods fail to preserve this…

Numerical Analysis · Mathematics 2019-12-30 Zheng Sun , Chi-Wang Shu

To achieve efficient and accurate long-time integration, we propose a fast, accurate, and stable high-order numerical method for solving fractional-in-space reaction-diffusion equations. The proposed method is explicit in nature and…

Numerical Analysis · Mathematics 2020-03-31 Almushaira Mustafa , Harish Bhatt

We study the application of the generalized convolution quadrature (gCQ) based on Runge--Kutta methods to approximate the solution of an important class of sectorial problems. The gCQ generalizes Lubich's original convolution quadrature…

Numerical Analysis · Mathematics 2025-06-27 Jing Guo , Maria Lopez-Fernandez

This manuscript introduces a fourth-order Runge-Kutta based implicit-explicit scheme in time along with compact fourth-order finite difference scheme in space for the solution of one-dimensional Kuramoto-Sivashinsky equation with periodic…

Numerical Analysis · Mathematics 2019-11-28 Harish Bhatt , Abhinandan Chowdhury

High order spatial discretizations with monotonicity properties are often desirable for the solution of hyperbolic PDEs. These methods can advantageously be coupled with high order strong stability preserving time discretizations. The…

Numerical Analysis · Mathematics 2014-03-27 Sigal Gottlieb , Zachary J. Grant , Daniel Higgs

We present adaptive finite difference ENO/WENO methods by adopting infinitely smooth radial basis functions (RBFs). This is a direct extension of the non-polynomial finite volume ENO/WENO method proposed by authors in \cite{GuoJung} to the…

Numerical Analysis · Mathematics 2017-05-23 Jingyang Guo , Jae-Hun Jung

A mixed accuracy framework for Runge--Kutta methods presented in Grant [JSC 2022] and applied to diagonally implicit Runge--Kutta (DIRK) methods can significantly speed up the computation by replacing the implicit solver by less expensive…

Recent developments have made it possible to overcome grid-based limitations of finite difference (FD) methods by adopting the kernel-based meshless framework using radial basis functions (RBFs). Such an approach provides a meshless…

Numerical Analysis · Mathematics 2019-01-07 Pankaj K Mishra , Gregory E Fasshauer , Mrinal K Sen , Leevan Ling

Cell collective migration plays a crucial role in a variety of physiological processes. In this work, we propose the Runge-Kutta random feature method to solve the nonlinear and strongly coupled multiphase flow problems of cells, in which…

Numerical Analysis · Mathematics 2024-12-10 Yangtao Deng , Qiaolin He

In this paper, we present a spectral method based on Radial Basis Functions (RBFs) for numerically solving the fully nonlinear 1D Serre Green-Naghdi equations. The approximation uses an RBF discretization in space and finite differences in…

Fluid Dynamics · Physics 2014-07-17 Maurice S. Fabien

A new projection method based on radial basis functions (RBFs) is presented for discretizing the incompressible unsteady Stokes equations in irregular geometries. The novelty of the method comes from the application of a new technique for…

Numerical Analysis · Mathematics 2015-09-21 Edward J. Fuselier , Varun Shankar , Grady B. Wright

A practical and new Runge--Kutta numerical scheme for stochastic differential equations is explored. Numerical examples demonstrate the strong convergence of the method. The first order strong convergence is then proved using Ito integrals…

Numerical Analysis · Mathematics 2012-10-04 A. J. Roberts

In this article we present a modification of classical Radial Basis Function (RBF) interpolation techniques aimed at reducing oscillations near discontinuities in one and two dimensions. Our approach introduces an adaptive mechanism by…

Numerical Analysis · Mathematics 2026-03-25 José Kuruc , David Levin , Pep Mulet , Juan Ruiz-Álvarez , Dionisio F. Yáñez

In \cite{ZH2019}, we developed a boundary treatment method for implicit-explicit (IMEX) Runge-Kutta (RK) methods for solving hyperbolic systems with source terms. Since IMEX RK methods include explicit ones as special cases, this boundary…

Numerical Analysis · Mathematics 2020-08-05 Weifeng Zhao , Juntao Huang , Steven J. Ruuth

The aim of this chapter is to show how option prices in jump-diffusion models can be computed using meshless methods based on Radial Basis Function (RBF) interpolation. The RBF technique is demonstrated by solving the partial…

Computational Finance · Quantitative Finance 2011-10-26 Ron T. L. Chan , Simon Hubbert

Recently, collocation based radial basis function (RBF) partition of unity methods (PUM) for solving partial differential equations have been formulated and investigated numerically and theoretically. When combined with stable evaluation…

Numerical Analysis · Mathematics 2017-02-24 Elisabeth Larsson , Victor Shcherbakov , Alfa Heryudono

Fractional-step methods are a popular and powerful divide-and-conquer approach for the numerical solution of differential equations. When the integrators of the fractional steps are Runge--Kutta methods, such methods can be written as…

Numerical Analysis · Mathematics 2023-01-25 Raymond J. Spiteri , Siqi Wei

We compare the three main types of high-order one-step initial value solvers: extrapolation, spectral deferred correction, and embedded Runge--Kutta pairs. We consider orders four through twelve, including both serial and parallel…

Numerical Analysis · Mathematics 2016-01-20 David I. Ketcheson , Umair bin Waheed

In this paper, we present error estimates of fully discrete Runge--Kutta discontinuous Galerkin (DG) schemes for linear time-dependent partial differential equations. The analysis applies to explicit Runge--Kutta time discretizations of any…

Numerical Analysis · Mathematics 2020-01-07 Zheng Sun , Chi-Wang Shu

Symmetry is a key property of numerical methods. The geometric properties of symmetric schemes make them an attractive option for integrating Hamiltonian systems, whilst their ability to exactly recover the initial condition without the…

Numerical Analysis · Mathematics 2026-05-12 Daniil Shmelev , Kurusch Ebrahimi-Fard , Nikolas Tapia , Cristopher Salvi