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Rare-Event Simulation

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Abstract

Rare events are events that are expected to occur infrequently or, more technically, those that have low probabilities (say, order of 10−3 or less) of occurring according to a probability model. In the context of uncertainty quantification, the rare events often correspond to failure of systems designed for high reliability, meaning that the system performance fails to meet some design or operation specifications. As reviewed in this section, computation of such rare-event probabilities is challenging. Analytical solutions are usually not available for nontrivial problems, and standard Monte Carlo simulation is computationally inefficient. Therefore, much research effort has focused on developing advanced stochastic simulation methods that are more efficient. In this section, we address the problem of estimating rare-event probabilities by Monte Carlo simulation, importance sampling, and subset simulation for highly reliable dynamic systems.

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References

  1. Asmussen, S., Glynn, P.W.: Stochastic Simulation: Algorithms and Analysis. Springer, New York (2010)

    MATH  Google Scholar 

  2. Au, S.K.: Importance sampling for elasto-plastic systems using adapted process with deterministic control. Int. J. Nonlinear Mech. 44, 189–198 (2009)

    Article  Google Scholar 

  3. Au, S.K., Beck, J.L.: First excursion probabilities for linear systems by very efficient importance sampling. Prob. Eng. Mech. 16, 193–207 (2001)

    Article  Google Scholar 

  4. Au, S.K., Beck, J.L.: Estimation of small failure probabilities in high dimensions by subset simulation. Prob. Eng. Mech. 16(4), 263–277 (2001)

    Article  Google Scholar 

  5. Au, S.K., Beck, J.L.: Importance sampling in high dimensions. Struct. Saf. 25(2), 139–163 (2003)

    Article  Google Scholar 

  6. Au, S.K., Ching, J., Beck, J.L.: Application of subset simulation methods to reliability benchmark problems. Struct. Saf. 29(3), 183–193 (2007)

    Article  Google Scholar 

  7. Au, S.K., Wang, Y.: Engineering Risk Assessment and Design with Subset Simulation. Wiley, Singapore (2014)

    Book  Google Scholar 

  8. Au, S.K., Wang, Z.H., Loa, S.M.: Compartment fire risk analysis by advanced Monte Carlo method. Eng. Struct. 29, 2381–2390 (2007)

    Article  Google Scholar 

  9. Bayes, T.: An essay towards solving a problem in the doctrine of chances. Philos. Trans. R. Soc. Lond. 53, 370–418 (1763). Reprinted in Biometrika 45, 296–315 (1989)

    Google Scholar 

  10. Beck, J.L.: Bayesian system identification based on probability logic. Struct. Control Health Monit. 17, 825–847 (2010)

    Article  Google Scholar 

  11. Beck, J.L., Au, S.K.: Reliability of Dynamic Systems using Stochastic Simulation. In: Proceedings of the 6th European Conference on Structural Dynamics, Paris (2005)

    Google Scholar 

  12. Botev, Z.I., Kroese, D.P.: Efficient Monte Carlo simulation via the generalized splitting method. Stat. Comput. 22(1), 1–16 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Bourinet, J.M., Deheeger, F., Lemaire, M.: Assessing small failure probabilities by combined subset simulation and support vector machines. Struct. Saf. 33(6), 343–353 (2011)

    Article  Google Scholar 

  14. Bucher, C., Bourgund, U.: A fast and efficient response surface approach for structural reliability problems. Struct. Saf. 7, 57–66 (1990)

    Article  Google Scholar 

  15. Bucklew, J.A.: Introduction to Rare Event Simulation. Springer Series in Statistics. Springer, New York (2004)

    Book  MATH  Google Scholar 

  16. Cadini, F., Avram, D., Pedroni, N., Zio, E.: Subset simulation of a reliability model for radioactive waste repository performance assessment. Reliab. Eng. Syst. Saf. 100, 75–83 (2012)

    Article  Google Scholar 

  17. Ching, J., Au, S.K., Beck, J.L.: Reliability estimation for dynamical systems subject to stochastic excitation using subset simulation with splitting. Comput. Methods Appl. Mech. Eng. 194, 1557–1579 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Ching, J., Beck, J.L., Au, S K.: Hybrid subset simulation method for reliability estimation of dynamical systems subject to stochastic excitation. Prob. Eng. Mech. 20, 199–214 (2005)

    Google Scholar 

  19. Deng, S., Giesecke, K., Lai, T.L.: Sequential importance sampling and resampling for dynamic portfolio credit risk. Oper. Res. 60(1), 78–91 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ditlevsen, O., Madsen, H.O.: Structural Reliability Methods. Wiley, Chichester/New York (1996)

    Google Scholar 

  21. Dubourg, V., Sudret, B., Deheeger, F.: Meta-model based importance sampling for structural reliability analysis. Prob. Eng. Mech. 33, 47–57 (2013)

    Article  Google Scholar 

  22. Dunn, W.L., Shultis, J.K.: Exploring Monte Carlo Methods. Elsevier, Amsterdam/Boston (2012)

    MATH  Google Scholar 

  23. Hurtado, J.: Structural Reliability: Statistical Learning Perspectives. Springer, Berlin/New York (2004)

    Book  MATH  Google Scholar 

  24. Jalayer, F., Beck, J.L.: Effects of two alternative representations of ground-motion uncertainty on probabilistic seismic demand assessment of structures. Earthq. Eng. Struct. Dyn. 37, 61–79 (2008)

    Article  Google Scholar 

  25. Jaynes, E.T.: Information theory and statistical mechanics. Phys. Rev. 106(4), 620–630 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  26. Jaynes, E.T.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  27. Johnson, C.: Numerical Solution of Partial Differential Equations by the Finite Element Method. Dover, Mineola (2009)

    MATH  Google Scholar 

  28. Kahn, H., Harris, T.E.: Estimation of particle transmission by random sampling. Natl. Bur. Stand. Appl. Math. Ser. 12, 27–30 (1951)

    Google Scholar 

  29. Kahn, H., Marshall, A. W.: Methods of reducing sample size in Monte Carlo computations. J. Oper. Res. Soc. Am. 1(5), 263–278 (1953)

    Google Scholar 

  30. Katafygiotis, L.S., Cheung, S.H.: A two-stage subset simulation-based approach for calculating the reliability of inelastic structural systems subjected to Gaussian random excitations. Comput. Methods Appl. Mech. Eng. 194, 1581–1595 (2005)

    Article  MATH  Google Scholar 

  31. Katafygiotis, L.S., Cheung, S.H.: Application of spherical subset simulation method and auxiliary domain method on a benchmark reliability study. Struct. Saf. 29(3), 194–207 (2007)

    Article  Google Scholar 

  32. Katafygiotis, L.S., Zuev, K.M.: Geometric insight into the challenges of solving high-dimensional reliability problems. Prob. Eng. Mech. 23, 208–218 (2008)

    Article  Google Scholar 

  33. Katafygiotis, L.S., Zuev, K.M.: Estimation of small failure probabilities in high dimensions by adaptive linked importance sampling. In: Proceedings of the COMPDYN-2007, Rethymno (2007)

    Google Scholar 

  34. Laplace, P.S.: Theorie Analytique des Probabilites. Courcier, Paris (1812)

    MATH  Google Scholar 

  35. Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2001)

    MATH  Google Scholar 

  36. Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)

    Article  Google Scholar 

  37. Metropolis, N., Ulam, S.: The Monte Carlo method. J. Am. Stat. Assoc. 44, 335–341 (1949)

    Article  MATH  Google Scholar 

  38. Metropolis, N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)

    Article  Google Scholar 

  39. Pellissetti, M.F., Schuëller, G.I., Pradlwarter, H.J., Calvi, A., Fransen, S., Klein, M.: Reliability analysis of spacecraft structures under static and dynamic loading. Comput. Struct. 84, 1313–1325 (2006)

    Article  Google Scholar 

  40. Papadimitriou, C., Beck, J.L., Katafygiotis, L.S.: Updating robust reliability using structural test data. Prob. Eng. Mech. 16, 103–113 (2001)

    Article  Google Scholar 

  41. Papadopoulos, V. Giovanis, D.G., Lagaros, N.D., Papadrakakis, M.: Accelerated subset simulation with neural networks for reliability analysis. Comput. Methods Appl. Mech. Eng. 223, 70–80 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  42. Pradlwarter, H.J., Schuëller, G.I., Melnik-Melnikov, P.G.: Reliability of MDOF-systems. Prob. Eng. Mech. 9, 235–43 (1994)

    Article  Google Scholar 

  43. Rackwitz, R.: Reliability analysis – a review and some perspectives. Struct. Saf. 32, 365–395 (2001)

    Article  Google Scholar 

  44. Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. Springer, New York (2004)

    Book  MATH  Google Scholar 

  45. Ross, S.M.: A First Course in Probability, 8th edn. Prentice Hall, Upper Saddle River (2009)

    MATH  Google Scholar 

  46. Santoso, A.M., Phoon, K.K., Quek, S.T.: Modified Metropolis-Hastings algorithm with reduced chain correlation for efficient subset simulation. Prob. Eng. Mech. 26, 331–341 (2011)

    Article  Google Scholar 

  47. Schuëller, G.I., Pradlwarter, H.J.: Benchmark study on reliability estimation in higher dimensions of structural systems – an overview. Struct. Saf. 29(3), 167–182 (2007)

    Article  Google Scholar 

  48. Schuëller, G.I., Pradlwarter, H.J., Koutsourelakis, P.S.: A critical appraisal of reliability estimation procedures for high dimensions. Prob. Eng. Mech. 19, 463–474 (2004)

    Article  Google Scholar 

  49. Sichani, M.T., Nielsen, S.R.K.: First passage probability estimation of wind turbines by Markov chain Monte Carlo. Struct. Infrastruct. Eng. 9, 1067–1079 (2013)

    Article  Google Scholar 

  50. Sparrow, C.: The Lorenz Equations: Bifurcations, Chaos, and Strange Attractors. Springer, New York (1982)

    Book  MATH  Google Scholar 

  51. Taflanidis, A.A., Beck, J.L.: Analytical approximation for stationary reliability of certain and uncertain linear dynamic systems with higher dimensional output. Earthq. Eng. Struct. Dyn. 35, 1247–1267 (2006)

    Article  Google Scholar 

  52. Thunnissen, D.P., Au, S.K., Tsuyuki, G.T.: Uncertainty quantification in estimating critical spacecraft component temperatures. AIAA J. Thermophys. Heat Transf. 21(2), 422–430 (2007)

    Article  Google Scholar 

  53. Valdebenito, M.A., Pradlwarter, H.J., Schuëller, G.I.: The role of the design point for calculating failure probabilities in view of dimensionality and structural nonlinearities. Struct. Saf. 32, 101–111 (2010)

    Article  Google Scholar 

  54. Villén-Altamirano, M., Villén-Altamirano, J.: Analysis of RESTART simulation: theoretical basis and sensitivity study. Eur. Trans. Telecommun. 13(4), 373–386 (2002)

    Article  MATH  Google Scholar 

  55. Zuev, K.: Subset simulation method for rare event estimation: an introduction. In: M. Beer et al. (Eds.) Encyclopedia of Earthquake Engineering. Springer, Berlin/Heidelberg (2015). Available on-line at http://www.springerreference.com/docs/html/chapterdbid/369348.html

  56. Zuev, K.M., Beck, J.L., Au. S.K., Katafygiotis, L.S.: Bayesian post-processor and other enhancements of subset simulation for estimating failure probabilities in high dimensions. Comput. Struct. 92–93, 283–296 (2012)

    Google Scholar 

  57. Zuev, K.M., Katafygiotis, L.S.: Modified Metropolis-Hastings algorithm with delayed rejection. Prob. Eng. Mech. 26, 405–412 (2011)

    Article  Google Scholar 

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Correspondence to James L. Beck .

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Beck, J.L., Zuev, K.M. (2017). Rare-Event Simulation. In: Ghanem, R., Higdon, D., Owhadi, H. (eds) Handbook of Uncertainty Quantification. Springer, Cham. https://doi.org/10.1007/978-3-319-12385-1_24

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