Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 5, 2017

A Partition-of-Unity Dual-Weighted Residual Approach for Multi-Objective Goal Functional Error Estimation Applied to Elliptic Problems

  • Bernhard Endtmayer and Thomas Wick EMAIL logo

Abstract

In this work, we design a posteriori error estimation and mesh adaptivity for multiple goal functionals. Our method is based on a dual-weighted residual approach in which localization is achieved in a variational form using a partition-of-unity. The key advantage is that the method is simple to implement and backward integration by parts is not required. For treating multiple goal functionals we employ the adjoint to the adjoint problem (i.e., a discrete error problem) and suggest an alternative way for its computation. Our algorithmic developments are substantiated for elliptic problems in terms of four different numerical tests that cover various types of challenges, such as singularities, different boundary conditions, and diverse goal functionals. Moreover, several computations with higher-order finite elements are performed.

Acknowledgements

We want to thank Professor Ulrich Langer for supporting this work at the Institute of Computational Mathematics at JKU Linz.

References

[1] M. Ainsworth and J. T. Oden, A posteriori error estimation in finite element analysis, Comput. Methods Appl. Mech. Engrg. 142 (1997), no. 1–2, 1–88. 10.1016/S0045-7825(96)01107-3Search in Google Scholar

[2] M. Ainsworth and J. T. Oden, A Posteriori Error Estimation in Finite Element Analysis, Pure Appl. Math. (New York), John Wiley & Sons, New York, 2000. 10.1002/9781118032824Search in Google Scholar

[3] G. Allaire, Analyse numerique et optimisation, Les Éditions de l’École Polytechnique, Palaiseau, 2005. Search in Google Scholar

[4] J. Andersson and H. Mikayelyan, The asymptotics of the curvature of the free discontinuity set near the cracktip for the minimizers of the Mumford–Shah functional in the plain. A revision, preprint (2015), https://arxiv.org/abs/1204.5328v2. Search in Google Scholar

[5] T. Apel, A.-M. Saendig and J. R. Whiteman, Graded mesh refinement and error estimates for finite element solutions of elliptic boundary value problems in non-smooth domains, Math. Methods Appl. Sci. 19 (1996), no. 1, 63–85. 10.1002/(SICI)1099-1476(19960110)19:1<63::AID-MMA764>3.0.CO;2-SSearch in Google Scholar

[6] W. Bangerth, R. Hartmann and G. Kanschat, deal.II – A general purpose object oriented finite element library, ACM Trans. Math. Softw. 33 (2007), no. 4, Article ID 24. 10.1145/1268776.1268779Search in Google Scholar

[7] W. Bangerth, T. Heister and G. Kanschat, deal. II Differential equations analysis library, Technical Reference (2013). Search in Google Scholar

[8] W. Bangerth and R. Rannacher, Adaptive Finite Element Methods for Differential Equations, Lectures in Math. ETH Zürich, Birkhäuser, Basel, 2003. 10.1007/978-3-0348-7605-6Search in Google Scholar

[9] R. Becker and R. Rannacher, Weighted a posteriori error control in FE methods, ENUMATH’97 (Heidelberg 1997), World Scientific, Singapore (1998), 621–637. Search in Google Scholar

[10] R. Becker and R. Rannacher, An optimal control approach to a posteriori error estimation in finite element methods, Acta Numer. 10 (2001), 1–102. 10.1017/S0962492901000010Search in Google Scholar

[11] A. Bonnet and G. David, Cracktip is a Global Mumford–Shah Minimizer, Astérisque 274, Société Mathématique de France, Paris, 2001. Search in Google Scholar

[12] M. Braack and A. Ern, A posteriori control of modeling errors and discretization errors, Multiscale Model. Simul. 1 (2003), no. 2, 221–238. 10.1137/S1540345902410482Search in Google Scholar

[13] D. Braess, Finite Elemente. Theory, Fast Solvers and Applications in Elasticity Theory, Springer, Berlin, 2007. 10.1017/CBO9780511618635Search in Google Scholar

[14] G. F. Carey and J. T. Oden, Finite Elements. Volume III: Computational Aspects, Texas Finite Elem., Prentice-Hall, Englewood Cliffs, 1984. Search in Google Scholar

[15] C. Carstensen and R. Verfürth, Edge residuals dominate a posteriori error estimates for low order finite element methods, SIAM J. Numer. Anal. 36 (1999), no. 5, 1571–1587. 10.1137/S003614299732334XSearch in Google Scholar

[16] P. G. Ciarlet, The Finite Element Method for Elliptic Problems, North-Holland, Amsterdam, 1987. Search in Google Scholar

[17] M. Dobrowolski, Angewandte Funktionalanalysis: Funktionalanalysis, Sobolev-Räume und elliptische Differentialgleichungen, Springer, Berlin, 2006. Search in Google Scholar

[18] K. Eriksson, D. Estep, P. Hansbo and C. Johnson, Introduction to adaptive methods for differential equations, Acta Numerica 1995, Cambridge University Press, Cambridge (1995), 105–158. 10.1017/S0962492900002531Search in Google Scholar

[19] D. Estep, M. Holst and M. Larson, Generalized green’s functions and the effective domain of influence, SIAM J. Sci. Comput. 26 (2005), no. 4, 1314–1339. 10.1137/S1064827502416319Search in Google Scholar

[20] L. C. Evans, Partial Differential Equations, American Mathematical Society, Providence, 2010. 10.1090/gsm/019Search in Google Scholar

[21] M. Giles and E. Süli, Adjoint methods for PDEs: A posteriori error analysis and postprocessing by duality, Acta Numer. 11 (2002), 145–236. 10.1017/CBO9780511550140.003Search in Google Scholar

[22] C. Großmann, H.-G. Roos and M. Stynes, Numerical Treatment of Partial Differential Equations, Springer, Berlin, 2007. 10.1007/978-3-540-71584-9Search in Google Scholar

[23] R. Hartmann, Multitarget error estimation and adaptivity in aerodynamic flow simulations, SIAM J. Sci. Comput. 31 (2008), no. 1, 708–731. 10.1137/070710962Search in Google Scholar

[24] R. Hartmann and P. Houston, Goal-oriented a posteriori error estimation for multiple target functionals, Hyperbolic Problems: Theory, Numerics, Applications, Springer, Berlin (2003), 579–588. 10.1007/978-3-642-55711-8_54Search in Google Scholar

[25] P. Houston, B. Senior and E. Sueli, hp-discontinuous galerkin finite element methods for hyperbolic problems: Error analysis and adaptivity, Internat. J. Numer. Methods Fluids 40 (2002), no. 1–2, 153–169. 10.1002/fld.271Search in Google Scholar

[26] D. Kuzmin and S. Korotov, Goal-oriented a posteriori error estimates for transport problems, Math. Comput. Simulation 80 (2010), no. 8, 1674–1683. 10.1016/j.matcom.2009.03.008Search in Google Scholar

[27] J. Oden and S. Prudhomme, On goal-oriented error estimation for elliptic problems: Application to the control of pointwise errors, Comput. Methods Appl. Mech. Engrg. 176 (1999), 313–331. 10.1016/S0045-7825(98)00343-0Search in Google Scholar

[28] J. Peraire and A. Patera, Bounds for linear-functional outputs of coercive partial differential Equations: Local indicators and adaptive refinement, Advances in Adaptive Computational Methods in Mechanics, Elsevier, Amsterdam (1998), 199–215. 10.1016/S0922-5382(98)80011-1Search in Google Scholar

[29] R. Rannacher and F.-T. Suttmeier, A feed-back approach to error control in finite element methods: Application to linear elasticity, Comput. Mech. 19 (1997), no. 5, 434–446. 10.1007/s004660050191Search in Google Scholar

[30] T. Richter, Goal-oriented error estimation for fluid-structure interaction problems, Comput. Methods Appl. Mech. Engrg. 223–224 (2012), 38–42. 10.1016/j.cma.2012.02.014Search in Google Scholar

[31] T. Richter and T. Wick, Variational localizations of the dual weighted residual estimator, J. Comput. Appl. Math. 279 (2015), no. 0, 192–208. 10.1016/j.cam.2014.11.008Search in Google Scholar

[32] A. Schroeder and A. Rademacher, Goal-oriented error control in adaptive mixed FEM for signorini’s problem, Comput. Methods Appl. Mech. Engrg. 200 (2011), no. 1–4, 345–355. 10.1016/j.cma.2010.08.015Search in Google Scholar

[33] E. van Brummelen, S. Zhuk and G. van Zwieten, Worst-case multi-objective error estimation and adaptivity, Comput. Methods Appl. Mech. Engrg. 313 (2017), 723–743. 10.1016/j.cma.2016.10.007Search in Google Scholar

[34] R. Verfürth, A Review of A Posteriori Error Estimation and Adaptive Mesh-Refinement Techniques, Wiley, New York, 1996. Search in Google Scholar

[35] S. Weisser and T. Wick, The dual-weighted residual estimator realized on polygonal meshes, Preprint Number 384, Saarland University, Department of Mathematics, 2016, https://www.math.uni-sb.de/service/preprints/. Search in Google Scholar

[36] T. Wick, Goal functional evaluations for phase-field fracture using PU-based DWR mesh adaptivity, Comput. Mech. 57 (2016), no. 6, 1017–1035. 10.1007/s00466-016-1275-1Search in Google Scholar

[37] J. Wloka, Partial Differential Equations, Cambridge University Press, Cambridge, 1987. 10.1017/CBO9781139171755Search in Google Scholar

[38] K. Zee, E. Brummelen, I. Akkerman and R. Borst, Goal-oriented error estimation and adaptivity for fluid-structure interaction using exact linearized adjoints, Comput. Methods Appl. Mech. Engrg. 200 (2011), 2738–2757. 10.1016/j.cma.2010.12.010Search in Google Scholar

Received: 2016-10-5
Revised: 2017-2-3
Accepted: 2017-2-24
Published Online: 2017-4-5
Published in Print: 2017-10-1

© 2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 1.5.2024 from https://www.degruyter.com/document/doi/10.1515/cmam-2017-0001/html
Scroll to top button