Superconvergence Study of the Direct Discontinuous Galerkin Method and Its Variations for Diffusion Equations

Yuqing Miao , Jue Yan , Xinghui Zhong

Communications on Applied Mathematics and Computation ›› 2021, Vol. 4 ›› Issue (1) : 180 -204.

PDF
Communications on Applied Mathematics and Computation ›› 2021, Vol. 4 ›› Issue (1) : 180 -204. DOI: 10.1007/s42967-020-00107-0
Original Paper

Superconvergence Study of the Direct Discontinuous Galerkin Method and Its Variations for Diffusion Equations

Author information +
History +
PDF

Abstract

In this paper, we apply the Fourier analysis technique to investigate superconvergence properties of the direct disontinuous Galerkin (DDG) method (Liu and Yan in SIAM J Numer Anal 47(1):475–698, 2009), the DDG method with the interface correction (DDGIC) (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010), the symmetric DDG method (Vidden and Yan in Comput Math 31(6):638–662, 2013), and the nonsymmetric DDG method (Yan in J Sci Comput 54(2):663–683, 2013). We also include the study of the interior penalty DG (IPDG) method, due to its close relation to DDG methods. Error estimates are carried out for both $P^2$ and $P^3$ polynomial approximations. By investigating the quantitative errors at the Lobatto points, we show that the DDGIC and symmetric DDG methods are superior, in the sense of obtaining $(k+2)$th superconvergence orders for both $P^2$ and $P^3$ approximations. Superconvergence order of $(k+2)$ is also observed for the IPDG method with $P^3$ polynomial approximations. The errors are sensitive to the choice of the numerical flux coefficient for even degree $P^2$ approximations, but are not for odd degree $P^3$ approximations. Numerical experiments are carried out at the same time and the numerical errors match well with the analytically estimated errors.

Cite this article

Download citation ▾
Yuqing Miao, Jue Yan, Xinghui Zhong. Superconvergence Study of the Direct Discontinuous Galerkin Method and Its Variations for Diffusion Equations. Communications on Applied Mathematics and Computation, 2021, 4(1): 180-204 DOI:10.1007/s42967-020-00107-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

Funding

National Natural Science Foundation of China(11871428)

Innovative Research Group Project of the National Natural Science Foundation of China(11621101)

National Science Foundation (US)(DMS-1620335)

Simons Foundation(637716)

AI Summary AI Mindmap
PDF

211

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/