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A Near-Optimal Hierarchical Estimate Based Adaptive Finite Element Method for Obstacle Problems

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Book cover Domain Decomposition Methods in Science and Engineering XIX

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 78))

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Abstract

Adaptive solvers are now widely used in numerical simulations of lots of problems for better accuracy with minimal computational cost. The reasons for choosing adaptive method for the problem (1) are two-folded. First, the grid in the contact zone is often not necessarily as fine as that in the non-contact zone. Secondly, the solution u may have singularity in some local areas.

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Bibliography

  1. M. Ainsworth and J.T. Oden. A Posteriori Error Estimation in Finite Element Analysis. Wiley, New York, NY, 2000.

    Google Scholar 

  2. R.E. Bank and R.K. Smith. A posteriori error estimates based on hierarchical bases. SIAM J. Numeric Anal., 30:921–935, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  3. E. Bänsch. Local mesh refinement in 2 and 3 dimensions. IMPACT Comput. Sci. Eng., 3:181–191, 1991.

    Article  MATH  Google Scholar 

  4. P. Binev, W. Dahmen, and R. DeVore. Adaptive finite element methods with convergence rates. Numer. Math., 97(2):219–268, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  5. F.A. Bornemann, B. Erdmann, and R. Kornhuber. A posteriori error estimates for elliptic problems in two and three space dimensions. SIAM J. Numer. Anal., 33:1188–1204, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  6. J.M. Cascon, C. Kreuzer, R.H. Nochetto, and K.G. Siebert. Quasi-optimal convergence rate for an adaptive finite element method. SIAM J. Numer. Anal., 46(5):2524–2550, 2008.

    Article  MATH  MathSciNet  Google Scholar 

  7. P. Deuflhard, P. Leinen, and H. Yserentant. Concepts of an adaptive hierarchical finite element code. IMPACT Comput. Sci. Eng., 1:3–35, 1989.

    Article  MATH  Google Scholar 

  8. W. Dörfler. A convergent adaptive algorithm for Poisson’s equation. SIAM J. Numer. Anal., 33:1106–1124, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  9. W. Dörfler and R.H. Nochetto. Small data oscillation implies the saturation assumption. Numer. Math., 91:1–12, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  10. F. Fierro and A. Veeser. A posteriori error estimators for regularized total variation of characteristic functions. SIAM J. Numer. Anal., 41(6):2032–2055 (electronic), 2003. ISSN 1095-7170.

    Google Scholar 

  11. R. Kornhuber. A posteriori error estimates for elliptic variational inequalities. Comput. Math. Appl., 31:49–60, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  12. R. Kornhuber. Adaptive Monotone Multigrid Methods for Nonlinear Variational Problems. Teubner, Stuttgart, 1997.

    MATH  Google Scholar 

  13. R. Kornhuber and Q. Zou. Efficient and reliable hierarchical error estimates for the discretization error of elliptic obstacle problems. Math. Comput., in press, 2010.

    Google Scholar 

  14. K. Mekchay and R. Nochetto. Convergence of adaptive finite element methods for general second order linear elliptic PDE. SIAM J. Numer. Anal., 43:1803–1827, 2005.

    Article  MATH  MathSciNet  Google Scholar 

  15. W.F. Mitchell. Unified Multilevel Adaptive Finite Element Methods for Elliptic Problems. PhD thesis, University of Illinois at Urbana-Champaign, 1988.

    Google Scholar 

  16. P. Morin, R.H. Nochetto, and K.G. Siebert. Data oscillation and convergence of adaptive fem. SIAM J. Numer. Anal., 38:466–488, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  17. P. Morin, R.H. Nochetto, and K.G. Siebert. Convergence of adaptive finite element methods. SIAM Rev., 44:631–658, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  18. K.G. Siebert and A. Veeser. A unilaterally constrained quadratic minimization with adaptive finite elements. SIAM J. Optim., 18:260–289, 2007.

    Article  MATH  MathSciNet  Google Scholar 

  19. R. Verfürth. A Review of a Posteriori Error Estimation and Adaptive Mesh–Refinement Techniques. Wiley-Teubner, Chichester, 1996.

    MATH  Google Scholar 

  20. O.C. Zienkiewicz, J.P. De, S.R. Gago, and D.W. Kelly. The hierarchical concept in finite element analysis. Comput. Struct., 16:53–65, 1983.

    Article  MATH  Google Scholar 

  21. Q. Zou. A near-optimal hierarchial analysis for elliptic obstacle problems. In preparation.

    Google Scholar 

  22. Q. Zou, A. Veeser, R. Kornhuber, and C. Gräser. Hierarchical error estimates for the energy functional in obstacle problems. Numer. Math., submitted, 2009.

    Google Scholar 

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Correspondence to Qingsong Zou .

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Zou, Q. (2011). A Near-Optimal Hierarchical Estimate Based Adaptive Finite Element Method for Obstacle Problems. In: Huang, Y., Kornhuber, R., Widlund, O., Xu, J. (eds) Domain Decomposition Methods in Science and Engineering XIX. Lecture Notes in Computational Science and Engineering, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11304-8_36

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