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A fictitious domain approach to the numerical solution of PDEs in stochastic domains

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Abstract

We present an efficient method for the numerical realization of elliptic PDEs in domains depending on random variables. Domains are bounded, and have finite fluctuations. The key feature is the combination of a fictitious domain approach and a polynomial chaos expansion. The PDE is solved in a larger, fixed domain (the fictitious domain), with the original boundary condition enforced via a Lagrange multiplier acting on a random manifold inside the new domain. A (generalized) Wiener expansion is invoked to convert such a stochastic problem into a deterministic one, depending on an extra set of real variables (the stochastic variables). Discretization is accomplished by standard mixed finite elements in the physical variables and a Galerkin projection method with numerical integration (which coincides with a collocation scheme) in the stochastic variables. A stability and convergence analysis of the method, as well as numerical results, are provided. The convergence is “spectral” in the polynomial chaos order, in any subdomain which does not contain the random boundaries.

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References

  1. Babuška, I., Nobile, F., Tempone, R.: A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J. Numer. Anal. (to appear)

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

    Article  MATH  MathSciNet  Google Scholar 

  3. Bertoluzza S. (1997). Interior estimates for the wavelet Galerkin method. Numer. Math. 78: 1–20

    Article  MATH  MathSciNet  Google Scholar 

  4. Brezzi F., Fortin M. (1991). Mixed and Hybrid Finite Element Methods. Springer, New York

    MATH  Google Scholar 

  5. Cameron R., Martin W. (1947). The orthogonal development of nonlinear functionals in series of Fourier-Hermite functionals. Ann. Math. 48: 385–392

    Article  MathSciNet  Google Scholar 

  6. Caflisch, R.E.: Monte Carlo and quasi-Monte Carlo methods. Acta Numer., 1–49 (1998)

  7. Canuto C., Hussaini M.Y., Quarteroni A., Zang T.A. (2006). Spectral Methods. Fundamentals in Single Domains. Springer, Berlin

    MATH  Google Scholar 

  8. Deb M.K., Babuška I., Oden J.T. (2001). Solution of stochastic partial differential equations using Galerkin finite element techniques. Comput. Methods Appl. Mech. Eng. 190: 6359–6372

    Article  MATH  Google Scholar 

  9. Ditzian Z., Totik V. (1987). Moduli of Smoothness. Springer, New York

    MATH  Google Scholar 

  10. Ghanem R., Spanos P.D. (1991). Stochastic Finite Elements—A Spectral Approach. Springer, Berlin

    MATH  Google Scholar 

  11. Gerstner T., Griebel M. (1998). Numerical integration using sparse grids. Numer. Algorithms 18: 209–232

    Article  MATH  MathSciNet  Google Scholar 

  12. Girault V., Glowinski R. (1995). Error analysis of a fictitious domain method applied to a Dirichlet problem. Japan J. Ind. Appl. Math. 12: 487–514

    Article  MATH  MathSciNet  Google Scholar 

  13. Glowinski R., Pan T.S., Périaux J. (1993). A least squares/fictitious domain method for mixed problems and Neumann problems. In: Lions, J.L., Baiocchi, C. (eds) Boundary Value Problems for Partial Differential Equations and Applications, pp 159–178. Masson, Paris

    Google Scholar 

  14. Glowinski R., Pan T., Periaux J. (1994). A fictitious domain method for Dirichlet problem and applications. Comput. Methods Appl. Mech. Eng. 111: 283–303

    Article  MATH  MathSciNet  Google Scholar 

  15. Haslinger J., Kozubek T. (2000). A fictitious domain approach for a class of Neumann boundary value problems with applications in shape optimization. East-West J Numer. Math. 8: 1–26

    MATH  MathSciNet  Google Scholar 

  16. Haslinger, J., Kozubek, T., Kucera, R., Peichl, G.: Projected Schur complement method for solving non-symmetric systems arising from a smooth fictitious domain approach. Numer. Linear Algebra Appl. (2007) (to appear)

  17. Haslinger J., Kozubek T., Kunisch K., Peichl G. (2003). Shape optimization and fictitious domain approach for solving free boundary problems of Bernoulli type. COAP 26: 231–251

    MATH  MathSciNet  Google Scholar 

  18. Haslinger J., Mäkinen R.A.E. (2003). Introduction to Shape Optimization, Theory, Approximation and Computation. SIAM, Philadelphia

    MATH  Google Scholar 

  19. Hosden, S., Walters, R.W., Perez, R.: A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations. AIAA Paper 2006-0891

  20. Kleiber M., Hien T.D. (1992). The Stochastic Finite Element Method. Basic Perturbation Technique and Computer Implementation. Wiley, Chichester

    MATH  Google Scholar 

  21. Kucera, R., Kozubek, T., Haslinger, J.: On solving non-symmetric saddle-point systems arising from fictitious domain approaches. In: Proceedings of PANM06, Prague, Czech Republic, pp. 165–171 (2006)

  22. Loève M. (1977). Probability Theory. Springer-Verlag, Berlin

    MATH  Google Scholar 

  23. Mastroianni G., Monegato G. (2006). Truncated approximation processes on unbounded intervals and their applications. Rend. Circ. Mat. Palermo 55: 123–139

    Article  MATH  MathSciNet  Google Scholar 

  24. Mathelin I., Hussaini M.Y., Zang T.A. (2005). Stochastic approaches to uncertainty quantification in CFD simulations. Numer. Algorithms 38: 209–236

    Article  MATH  MathSciNet  Google Scholar 

  25. Matthies, H.G., Keese, A.: Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations, pp. 1295–1331 in [28]

  26. Mommer M.S. (2006). A smoothness preserving fictitious domain method for elliptic boundary-value problems. IMA J. Numer. Anal. 26: 503–524

    Article  MATH  MathSciNet  Google Scholar 

  27. Oksendal B. (1998). Stochastic Differential Equations—An Introduction with Applications. Springer, Berlin

    Google Scholar 

  28. Schuëller, G.I. (ed.): Computational Methods in Stochastic Mechanics and Reliability Analysis. Special Issue 12–16, Comput. Methods Appl. Mech. Eng. 194 (2005)

  29. Schwab Ch., Todor R.A. (2003). Sparse finite elements for stochastic elliptic problems-higher order moments. Computing 71: 43–63

    Article  MATH  MathSciNet  Google Scholar 

  30. Todor, R.A., Schwab, Ch.: Convergence rates for sparse chaos approximations of elliptic problems with stochastic coefficients. Research Rep. No. 2006-05, Seminar für Angewandte Mathematik, ETH Zurich

  31. Walters, R.W.: Towards stochastic fluid mechanics via polynomial chaos. AIAA Paper 2003-0413

  32. Wiener N. (1938). The homogeneous chaos. Am. J. Math. 60: 897–936

    Article  MATH  MathSciNet  Google Scholar 

  33. Xiu D., Karniadakis G.E. (2002). The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24: 619–644

    Article  MATH  MathSciNet  Google Scholar 

  34. Xiu D., Karniadakis G.E. (2003). Modeling uncertainty in flow simulation via generalized polynomial chaos. J. Comput. Phys. 187: 137–167

    Article  MATH  MathSciNet  Google Scholar 

  35. Xiu D., Tartakovsky D.M. (2006). Numerical methods for differential equations in random domains. SIAM J. Sci. Comput. 28: 1167–1185

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to Claudio Canuto.

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Canuto, C., Kozubek, T. A fictitious domain approach to the numerical solution of PDEs in stochastic domains. Numer. Math. 107, 257–293 (2007). https://doi.org/10.1007/s00211-007-0086-x

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  • DOI: https://doi.org/10.1007/s00211-007-0086-x

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