Mitigating Gibbs Phenomenon: A Localized Padé-Chebyshev Approach and Its Conservation Law Applications
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Keywords:
Chebyshev expansion, Local Pad\'e approximation, Gibbs phenomenon, Numerical scheme for scalar conservation lawsAbstract
Approximating non-smooth functions presents a significant challenge due to the emergence of unwanted oscillations near discontinuities, commonly known as Gibbs' phenomena. Traditional methods like finite Fourier or Chebyshev representations only achieve convergence on the order of $O(1)$. A promising avenue in addressing this issue lies in nonlinear and essentially non-oscillatory approximation techniques, such as rational or Padé approximation. A recent and notable endeavor to mitigate Gibbs' oscillations is through singular Padé-Chebyshev approximation. However, a drawback of this approach is the requirement to specify the discontinuity location within the algorithm, which is often unknown in practical applications. To tackle this obstacle, we propose a localized Pad\'e-Chebyshev approximation method. Fortunately, our efforts yield success; the proposed localized variant effectively captures jump locations in non-smooth functions while maintaining an essentially non-oscillatory character. Furthermore, we employ Padé-Chebyshev approximation within a finite volume framework to address scalar hyperbolic conservation laws. Remarkably, the resulting rational numerical scheme demonstrates stability regardless of wave propagation direction. Consequently, we introduce a central rational numerical scheme for scalar hyperbolic conservation laws, offering robust and accurate computation of solutions.
References
N. N. Abdelmalek and W. A. Malek. Numerical linear approximation in C. Chapman & Hall/CRCNumerical Analysis
and Scientific Computing. CRCPress, Boca Raton, FL, 2008. With 1 CD-ROM (Windows).
Akansha. Decayanalysis of bivariate chebyshev coefficients for functions with limited regularity. Results in Applied
Mathematics, 22:100449, 2024.
A Akansha. Addressing the impact of localized training data in graph neural networks. In 2023 7th International
Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA), pages 1–6. IEEE, 2023.
S Akansha. Conditional shift-robust conformal prediction for graph neural network. arXiv preprint arXiv:2405.11968,
S Akansha.Exploring chebyshev polynomial approximations: Error estimates for functions of bounded variation. arXiv
preprint arXiv:2404.18723, 2024.
S. Akansha and S. Baskar.Adaptive padé-chebyshev type approximation to piecewise smooth functions, 2019.
Singh Akansha. Over-squashing in graph neural networks:A comprehensive survey. arXiv preprint arXiv:2308.15568,
F. Arandiga, A. Cohen, R. Donat, and N. Dyn. Interpolation and approximation of piecewise smooth functions. SIAM
J. Numer. Anal., 43(1):41–57, 2005.
G. A. Baker, Jr.The theory and application of the Padé approximant method. In Advances in Theoretical Physics, Vol.
, pages 1–58. Academic Press, New York, 1965.
G. A. Baker, Jr.and G.-M.Peter. Padé approximants, volume 59 of Encyclopedia of Mathematics and its Applications.
Cambridge University Press, Cambridge, second edition, 1996.
E. W. Cheney. Introduction to approximation theory. McGraw-Hill Book Co., New York-Toronto, Ont.-London, 1966.
R. A. DeVore. Nonlinear approximation. In Acta numerica, 1998, volume 7 of Acta Numer., pages 51–150. Cambridge
Univ. Press, Cambridge, 1998.
R. A. DeVore. Nonlinear approximation and its applications. In Multiscale, nonlinear and adaptive approximation,
pages 169–201. Springer, Berlin, 2009.
R. A. DeVoreand G. G. Lorentz. Constructive approximation, volume 303 of Grundlehren der Mathematischen
Wissenschaften[Fundamental Principlesof Mathematical Sciences]. Springer-Verlag, Berlin, 1993.
R. A. DeVore and V. N. Temlyakov. Nonlinear approximation in finite-dimensional spaces. J. Complexity, 13(4):489–
, 1997.
T. A. Driscoll and B. Fornberg.A Padé-based algorithm for overcoming the Gibbs phenomenon. Numer. Algorithms,
(1):77–92, 2001.
D. Gottlieband C.-W. Shu. On the Gibbs phenomenon and its resolution. SIAM Rev., 39(4):644–668, 1997.
W. Hao, J. D. Hauenstein,C.-W. Shu, A. J. Sommese, Z. Xu, and Y.-T. Zhang. A homotopy method based on WENO
schemes for solving steady state problems of hyperbolic conservation laws. J. Comput. Phys., 250:332–346, 2013.
A. Harten.High resolution schemes for hyperbolic conservation laws. J. Comput. Phys., 49(3):357–393, 1983.
A.Harten. On a class of high resolution total-variation-stable finite-difference schemes. SIAM J. Numer. Anal.,
(1):1–23, 1984. With an appendix by Peter D. Lax.
A. Harten, B. Engquist, S. Osher, and S. R. Chakravarthy. Uniformly high-order accurate essentially nonoscillatory
schemes. III. J. Comput. Phys., 71(2):231–303, 1987.
A. Harten and S. Osher.Uniformly high-order accurate nonoscillatory schemes. I. SIAM J. Numer. Anal., 24(2):279–
, 1987.
G.-S. Jiang and C.-W. Shu.Efficient implementation of weighted ENO schemes. J. Comput. Phys., 126(1):202–228,
Y. Jiang, C.-W. Shu, and M. Zhang.An alternative formulation of finite difference weighted ENO schemes with LaxWendroff time discretization for conservation laws. SIAM J. Sci. Comput., 35(2):A1137–A1160, 2013.
S. M. Kaber and Y. Maday. Analysis of some Padé-Chebyshev approximants. SIAM J. Numer. Anal., 43(1):437–454,
S. Karni and A. Kurganov.Local error analysis for approximate solutions of hyperbolic conservation laws. Adv. Comput.
Math., 22(1):79–99, 2005.
S. Karni,A. Kurganov, andG.Petrova.A smoothness indicator for adaptive algorithms for hyperbolic systems. J.
Comput. Phys., 178(2):323–341, 2002.
Zhengpin Li and JianWang.Spectral graph neural networks with generalized laguerre approximation. In ICASSP
–2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7760–7764.
IEEE, 2024.
X.-D. Liu, S. Osher, and T. Chan.Weighted essentially non-oscillatory schemes. J. Comput. Phys., 115(1):200–212,
J. C. Mason and D. C. Handscomb. Chebyshev polynomials. Chapman & Hall/CRC, Boca Raton, FL, 2003.
G.Nürnberger. Approximation by spline functions. Springer-Verlag, Berlin, 1989.
G. M. Phillips. Interpolation and approximation by polynomials. CMS Books in Mathematics/Ouvrages de
Mathématiques de la SMC, 14. Springer-Verlag, New York, 2003.
P. M. Prenter. Splines and variational methods. Wiley Classics Library. John Wiley & Sons, Inc., New York, 1989.
Reprint of the 1975 original, A Wiley-Interscience Publication.
Akansha S. Piecewise padé-chebyshev reconstruction of bivariate piecewise smooth functions, 2021.
A. Sard. Linear approximation. American Mathematical Society, Providence, R.I., 1963.
I. J. Schoenberg. Cardinal spline interpolation. Society for Industrial and Applied Mathematics, Philadelphia, Pa.,
Conference Board of the Mathematical Sciences Regional Conference Series in Applied Mathematics, No. 12.
H.-T. Shimand C.-H. Park. Survey of gibbs phenomenon from fourier series to hybrid sampling series. J. Complexity,
(1-2-3):719–736, 2005.
C.-W.Shu. Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation
laws. In Advanced numerical approximation of nonlinear hyperbolic equations (Cetraro, 1997), volume 1697 of Lecture
Notes in Math., pages 325–432. Springer, Berlin, 1998.
C.-W. Shu. High order weighted essentially nonoscillatory schemes for convection dominated problems. SIAM Rev.,
(1):82–126, 2009.
C.-W. Shu and S. Osher. Efficient implementation of essentially nonoscillatory shock-capturing schemes. J. Comput.
Phys., 77(2):439–471, 1988.
C.-W. Shu and S. Osher. Efficient implementation of essentially nonoscillatory shock-capturing schemes. II. J. Comput.
Phys., 83(1):32–78, 1989.
E. Tadmor. Filters, mollifiers and the computation of the Gibbs phenomenon. Acta Numer., 16:305–378, 2007.
A. L.Tampos and J. E. C. Lope. Overcoming Gibbs phenomenon in Padé-Chebyshev approximation. Matimyás Mat.,
(2-3):127–135, 2007.
A.L. Tampos, J. E. C. Lope, and J. S. Hesthaven. Accurate reconstruction of discontinuous functions using the singular
Padé-Chebyshev method. IAENG Int. J. Appl. Math., 42(4):242–249, 2012.
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