Optimized low-dispersion and low-dissipation two-derivative Runge–Kutta method for wave equations

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Optimized low-dispersion and low-dissipation two-derivative Runge–Kutta method for wave equations. / Krivovichev, Gerasim V.

In: Journal of Applied Mathematics and Computing, Vol. 63, No. 1-2, 01.06.2020, p. 787-811.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{cb382d7649904c8f98c564724e088b33,

title = "Optimized low-dispersion and low-dissipation two-derivative Runge–Kutta method for wave equations",

abstract = "The paper is devoted to the optimization of the explicit two-derivative sixth-order Runge–Kutta method in order to obtain low dissipation and dispersion errors. The method is dependent on two free parameters, used for the optimization. The optimized method demonstrates the lowest dispersion error in comparison with other widely-used high-order Runge–Kutta methods for hyperbolic problems. The efficiency of the method is demonstrated on the solutions of problems for five linear and nonlinear partial differential equations by the method-of-lines. Spatial derivatives are discretized by finite differences and Petrov–Galerkin approximations. The work-precision and error—CPU time plots, as the dependence of CPU time on space grid resolution, are considered. Also, the optimized method compared with other two-derivative methods. In most examples, the optimized method has better properties, especially for the cases of computations with adaptive stepsize control at high accuracy. The lowest CPU time takes place for the optimized method, especially in the cases of fine space grids.",

keywords = "Dispersion, Dissipation, Method-of-lines, Optimization, Runge–Kutta methods, Wave equation",

author = "Krivovichev, {Gerasim V.}",

year = "2020",

month = jun,

day = "1",

doi = "10.1007/s12190-020-01339-2",

language = "English",

volume = "63",

pages = "787--811",

journal = "Journal of Applied Mathematics and Computing",

issn = "1598-5865",

publisher = "Springer Nature",

number = "1-2",

}

RIS

TY - JOUR

T1 - Optimized low-dispersion and low-dissipation two-derivative Runge–Kutta method for wave equations

AU - Krivovichev, Gerasim V.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - The paper is devoted to the optimization of the explicit two-derivative sixth-order Runge–Kutta method in order to obtain low dissipation and dispersion errors. The method is dependent on two free parameters, used for the optimization. The optimized method demonstrates the lowest dispersion error in comparison with other widely-used high-order Runge–Kutta methods for hyperbolic problems. The efficiency of the method is demonstrated on the solutions of problems for five linear and nonlinear partial differential equations by the method-of-lines. Spatial derivatives are discretized by finite differences and Petrov–Galerkin approximations. The work-precision and error—CPU time plots, as the dependence of CPU time on space grid resolution, are considered. Also, the optimized method compared with other two-derivative methods. In most examples, the optimized method has better properties, especially for the cases of computations with adaptive stepsize control at high accuracy. The lowest CPU time takes place for the optimized method, especially in the cases of fine space grids.

AB - The paper is devoted to the optimization of the explicit two-derivative sixth-order Runge–Kutta method in order to obtain low dissipation and dispersion errors. The method is dependent on two free parameters, used for the optimization. The optimized method demonstrates the lowest dispersion error in comparison with other widely-used high-order Runge–Kutta methods for hyperbolic problems. The efficiency of the method is demonstrated on the solutions of problems for five linear and nonlinear partial differential equations by the method-of-lines. Spatial derivatives are discretized by finite differences and Petrov–Galerkin approximations. The work-precision and error—CPU time plots, as the dependence of CPU time on space grid resolution, are considered. Also, the optimized method compared with other two-derivative methods. In most examples, the optimized method has better properties, especially for the cases of computations with adaptive stepsize control at high accuracy. The lowest CPU time takes place for the optimized method, especially in the cases of fine space grids.

KW - Dispersion

KW - Dissipation

KW - Method-of-lines

KW - Optimization

KW - Runge–Kutta methods

KW - Wave equation

UR - http://www.scopus.com/inward/record.url?scp=85082200908&partnerID=8YFLogxK

U2 - 10.1007/s12190-020-01339-2

DO - 10.1007/s12190-020-01339-2

M3 - Article

AN - SCOPUS:85082200908

VL - 63

SP - 787

EP - 811

JO - Journal of Applied Mathematics and Computing

JF - Journal of Applied Mathematics and Computing

SN - 1598-5865

IS - 1-2

ER -

ID: 60401821