Skip to main content

Partitioned Solution of Coupled Stochastic Problems

  • Chapter
  • First Online:
Numerical Simulations of Coupled Problems in Engineering

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 33))

  • 1988 Accesses

Abstract

This work is concerned with the propagation of uncertainty across coupled problems with high-dimensional random inputs. A stochastic model reduction approach based on low-rank separated representations is proposed for the partitioned treatment of the uncertainty space. The construction of the coupled solution is achieved though a sequence of approximations with respect to the dimensionality of the random inputs associated with each individual subproblem and not the combined dimensionality, hence drastically reducing the overall computational cost. The coupling between the sub-domain solutions is done via the classical Finite Element Tearing and Interconnecting (FETI) method, thus providing a well suited framework for parallel computing. A high-dimensional stochastic problem, a coupled 2D elliptic PDE with random diffusion coefficient, has been considered in this paper to study the performance and accuracy of the proposed stochastic coupling approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Babuška I, Chatzipantelidis P (2002) On solving elliptic stochastic partial differential equations. Comput Methods Appl Mech Eng 191(37–38):4093–4122

    Article  MATH  Google Scholar 

  2. Babuška I, Tempone R, Zouraris G (2004) Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J Numer Anal 42(2):800–825

    Article  MATH  MathSciNet  Google Scholar 

  3. Bieri M, Andreev R, Schwab C (2010) Sparse tensor discretization of elliptic sPDEs. SIAM J Sci Comput 31:4281–4304

    Article  MathSciNet  Google Scholar 

  4. Doostan A, Iaccarino G (2009) A least-squares approximation of partial differential equations with high-dimensional random inputs. J Comput Phys 228(12):4332–4345

    Article  MATH  MathSciNet  Google Scholar 

  5. Doostan A, Owhadi H (2011) A non-adapted sparse approximation of PDEs with stochastic inputs. J Comput Phys 230:3015–3034

    Article  MATH  MathSciNet  Google Scholar 

  6. Doostan A, Ghanem R, Red-Horse J (2007) Stochastic model reduction for chaos representations. Comput Meth Appl Mech Eng 196(37–40):3951–3966

    Article  MATH  MathSciNet  Google Scholar 

  7. Farhat C, Roux F (1991) A method of finite element tearing and interconnecting and its parallel solution algorithm. Int J Numer Meth Eng 32:1205–1227

    Article  MATH  Google Scholar 

  8. Farhat C, Lesoinne M, LeTallec P, Pierson K, Rixen D (2001) FETI-DP: a dual-primal unified FETI method-part i: a faster alternative to the two-level FETI method. Int J Num Meth Eng 50:1523–1544

    Article  MATH  MathSciNet  Google Scholar 

  9. Foo J, Karniadakis G (2010) Multi-element probabilistic collocation method in high dimensions. J Comput Phys 229(5):1536–1557

    Article  MATH  MathSciNet  Google Scholar 

  10. Ghosh D, Avery P, Farhat C (2009) A FETI-preconditioned congugate gradient method for large-scale stochastic finite element problems. Int J Numer Meth Eng 80:914–931

    Article  MATH  MathSciNet  Google Scholar 

  11. Giraldi L, Litvinenko A, Liu D, Matthies HG, Nouy A (2013) To be or not to be intrusive? the solution of parametric and stochastic equations–the “plain vanilla” Galerkin case. arXiv:1309.1617v1:[math.NA]. http://arxiv.org/abs/1309.1617v1

  12. Hadigol M, Doostan A, Matthies HG, Niekamp R (2013) Partitioned treatment of uncertainty in coupled domain problems: a separated representation approach. arXiv:1305.6818:[math.PR]. http://arxiv.org/abs/1305.6818

  13. Khoromskij B, Schwab C (2011) Tensor-structured Galerkin approximation of parametric and stochastic elliptic PDEs. SIAM J Sci Comput 33(1):364–385

    Article  MATH  MathSciNet  Google Scholar 

  14. Le Maître O, Knio O (2010) Spectral methods for uncertainty quantification with applications to computational fluid dynamics. Springer, New York

    Google Scholar 

  15. Ma X, Zabaras N (2009) An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J Comput Phys 228:3084–3113

    Article  MATH  MathSciNet  Google Scholar 

  16. Matthies HG (2008) Stochastic finite elements: computational approaches to stochastic partial differential equations. Z Angew Math Mech 88:849–873

    Article  MATH  MathSciNet  Google Scholar 

  17. Matthies HG, Keese A (2005) Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations. Comput Meth Appl Mech Eng 4:1295–1331

    Article  MathSciNet  Google Scholar 

  18. Matthies HG, Niekamp R, Steindorf J (2006) Algorithms for strong coupling procedures. Comput Meth Appl Mech Eng 195(17–18):2028–2049. doi:10.1016/j.cma.2004.11.032

  19. Matthies HG, Litvinenko A, Pajonk O, Rosić BV, Zander E (2012) Parametric and uncertainty computations with tensor product representations. In: Dienstfrey A, Boisvert R (eds) Uncertainty quantification in scientific computing. IFIP Advances in Information and Communication Technology, vol 377. Springer, Berlin, pp 139–150. doi:10.1007/978-3-642-32677-6

  20. Najm H (2009) Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics. Ann Rev 41(1):35–52

    MathSciNet  Google Scholar 

  21. Nobile F, Tempone R, Webster C (2008) An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J Numer Anal 46(5):2411–2442

    Article  MATH  MathSciNet  Google Scholar 

  22. Nouy A (2010) Proper generalized decompositions and separated representations for the numerical solution of high dimensional stochastic problems. Arch Comput Meth Eng 17:403–434

    Article  MATH  MathSciNet  Google Scholar 

  23. Park KC, Felippa CA (1998) A variational framework for solution method developments in structural mechanics. J Appl Mech 56(1):242–249

    Article  MathSciNet  Google Scholar 

  24. Subber W, Sarkar A (2012) Domain decomposition method of stochastic PDEs: a two-level scalable preconditioner. J Phys: Conf Ser 341(1):012033

    Google Scholar 

  25. Xiu D (2009) Fast numerical methods for stochastic computations: a review. Commun Comput Phys 5(2–4):242–272

    MathSciNet  Google Scholar 

  26. Xiu D (2010) Numerical methods for stochastic computations: a spectral method approach. Princeton University Press, Princeton

    Google Scholar 

  27. Xiu D, Hesthaven J (2005) High-order collocation methods for differential equations with random inputs. SIAM J Sci Comput 27(3):1118–1139

    Article  MATH  MathSciNet  Google Scholar 

  28. Zhang Z, Choi M, Karniadakis GE (2009) Anchor points matter in ANOVA decomposition. In: Spectral and higher order methods for partial differential equations. Lecture Notes in Computational Science and Engineering, Trondheim, pp 347–355

    Google Scholar 

Download references

Acknowledgments

The authors are indebted for the fruitful discussions they had with Prof. K.C. Park from University of Colorado, Boulder. AD gratefully acknowledges the financial support of the Department of Energy under Advanced Scientific Computing Research Early Career Research Award DE-SC0006402. MH’s work was supported by the National Science Foundation grant CMMI-1201207. The work of HGM and RN has been partly supported by the German Research Foundation “Deutsche Forschungsgemeinschaft” (DFG).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hermann G. Matthies .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hadigol, M., Doostan, A., Matthies, H.G., Niekamp, R. (2014). Partitioned Solution of Coupled Stochastic Problems. In: Idelsohn, S. (eds) Numerical Simulations of Coupled Problems in Engineering. Computational Methods in Applied Sciences, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-06136-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06136-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06135-1

  • Online ISBN: 978-3-319-06136-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics