Skip to main content

OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation

  • Reference work entry
  • First Online:
Handbook of Uncertainty Quantification

Abstract

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group, and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an open-source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for open-source treatment of uncertainty, Risk ’N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency , genericity , modularity, and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: OpenTURNS is an open- source software under the LGPL license that presents itself as a C++ library and a Python TUI and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis, and metamodeling. A section also explains the generic wrappers’ way to link OpenTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dike that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,099.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,399.99
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. Airbus, EDF, Phimeca: Developer’s guide, OpenTURNS 1.4 (2014). http://openturns.org

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

    Article  Google Scholar 

  3. Barate, R.: Calcul haute performance avec OpenTURNS, workshop du GdR MASCOT-NUM, Quantification d’incertitude et calcul intensif. http://www.gdr-mascotnum.fr/media/openturns-hpc-2013-03-28.pdf (2013)

  4. Berg, I.: muparser, http://muparser.beltoforion.de, fast Math Parser Library (2014)

  5. Berger, J. (ed.): Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985)

    MATH  Google Scholar 

  6. Blatman, G.: Adaptative sparse polynomial chaos expansions for uncertainty propagation and sensitivity anaysis. PhD thesis, Clermont University (2009)

    Google Scholar 

  7. Blatman, G., Sudret, B.: Adaptive sparse polynomial chaos expansion based on least angle regression. J. Comput. Phys. 230, 2345–2367 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  8. Butucea, C., Delmas, J., Dutfoy, A., Fischer, R.: Maximum entropy copula with given diagonal section. J. Multivar. Anal. 137, 61–81 (2015)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  10. Dutfoy, A., Dutka-Malen, I., Pasanisi, A., Lebrun, R., Mangeant, F., Gupta, J.S., Pendola, M., Yalamas, T.: OpenTURNS, an open source initiative to treat uncertainties, Risks’N statistics in a structured industrial approach. In: Proceedings of 41èmes Journées de Statistique, Bordeaux (2009)

    Google Scholar 

  11. Fang, K.T., Li, R., Sudjianto, A.: Design and Modeling for Computer Experiments. Chapman & Hall/CRC, Boca Raton (2006)

    MATH  Google Scholar 

  12. gum08: JCGM 100-2008 – Evaluation of measurement data – guide to the expression of uncertainty in measurement. JCGM (2008)

    Google Scholar 

  13. Hyndman, R., Shang, H.: Rainbow plots, bagplots, and boxplots for functional data. J. Comput. Graph. Stat. 19, 29–45 (2010)

    Article  MathSciNet  Google Scholar 

  14. Iooss, B., Lemaître, P.: A review on global sensitivity analysis methods. In: Meloni, C., Dellino, G. (eds.) Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications. Springer, New York (2015)

    Google Scholar 

  15. Kurowicka, D., Cooke, R.: Uncertainty Analysis with High Dimensional Dependence Modelling. Wiley, Chichester/Hoboken (2006)

    Book  MATH  Google Scholar 

  16. Lebrun, R., Dutfoy, A.: Do rosenblatt and nataf isoprobabilistic transformations really differ? Probab. Eng. Mech. 24, 577–584 (2009)

    Article  Google Scholar 

  17. Lebrun, R., Dutfoy, A.: A generalization of the nataf transformation to distributions with elliptical copula. Probab. Eng. Mech. 24, 172–178 (2009)

    Article  Google Scholar 

  18. Lebrun, R., Dutfoy, A.: An innovating analysis of the nataf transformation from the viewpoint of copula. Probab. Eng. Mech. 24, 312–320 (2009)

    Article  Google Scholar 

  19. Lebrun, R., Dutfoy, A.: A practical approach to dependence modelling using copulas. J. Risk Reliab. 223 (04), 347–361 (2009)

    Google Scholar 

  20. Lebrun, R., Dutfoy, A.: Copulas for order statistics with prescribed margins. J. Multivar. Anal. 128, 120–133 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Lemaire, M.: Structural Reliability. Wiley, Hoboken (2009)

    Book  Google Scholar 

  22. Liberty, L.: Ev3: a library for symbolic computation in c++ using n-ary trees, http://www.lix.polytechnique.fr/~liberti/Ev3.pdf (2003)

  23. Marin, J.M., Robert, C. (eds.): Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New York (2007)

    MATH  Google Scholar 

  24. Marrel, A., Iooss, B., Van Dorpe, F., Volkova, E.: An efficient methodology for modeling complex computer codes with Gaussian processes. Comput. Stat. Data Anal. 52, 4731–4744 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  25. Munoz-Zuniga, M., Garnier, J., Remy, E.: Adaptive directional stratification for controlled estimation of the probability of a rare event. Reliab. Eng. Syst. Saf. 96, 1691–1712 (2011)

    Article  MATH  Google Scholar 

  26. Nash, S.: A survey of truncated-newton methods. J. Comput. Appl. Math. 124, 45–59 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  27. OPEN CASCADE S.: Salome: the open source integration platform for numerical simulation. http://www.salome-platform.org (2006)

  28. Pasanisi, A.: Uncertainty analysis and decision-aid: methodological, technical and managerial contributions to engineering and R&D studies. Habilitation Thesis of Université de Technologie de Compiègne, France https://tel.archives-ouvertes.fr/tel-01002915 (2014)

  29. Pasanisi, A., Dutfoy, A.: An industrial viewpoint on uncertainty quantification in simulation: stakes, methods, tools, examples. In: Dienstfrey, A., Boisvert, R. (eds.) Uncertainty Quantification in Scientific Computing – 10th IFIP WG 2.5 Working Conference, WoCoUQ 2011, Boulder, 1–4 Aug 2011. IFIP Advances in Information and Communication Technology, vol. 377, pp. 27–45. Springer, Berlin (2012)

    Google Scholar 

  30. Rasmussen, C., Williams, C., Dietterich, T.: Gaussian Processes for Machine Learning. MIT, Cambridge (2006)

    Google Scholar 

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

    Book  MATH  Google Scholar 

  32. Rubinstein, R.: Simulation and the Monte-Carlo Methods. Wiley, New York (1981)

    Book  MATH  Google Scholar 

  33. Sacks, J., Welch, W., Mitchell, T., Wynn, H.: Design and analysis of computer experiments. Stat. Sci. 4, 409–435 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  34. Saltelli, A.: Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297 (2002)

    Article  MATH  Google Scholar 

  35. Saltelli, A., Tarantola, S., Chan, K.: A quantitative, model-independent method for global sensitivity analysis of model output. Technometrics 41, 39–56 (1999)

    Article  Google Scholar 

  36. Saltelli, A., Chan, K., Scott, E. (eds.): Sensitivity Analysis. Wiley Series in Probability and Statistics. Wiley, Chichester/New York (2000)

    MATH  Google Scholar 

  37. Santner, T., Williams, B., Notz, W.: The Design and Analysis of Computer Experiments. Springer, New York (2003)

    Book  MATH  Google Scholar 

  38. Sudret, B.: Global sensitivity analysis using polynomial chaos expansion. Reliab. Eng. Syst. Saf. 93, 964–979 (2008)

    Article  Google Scholar 

  39. Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics, Philadelphia (2005)

    Book  MATH  Google Scholar 

  40. Top 500 Supercomputer Sites: Zumbrota http://www.top500.org/system/177726, BlueGene/Q, Power BQC 16C 1.60GHz, Custom (2014)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michaël Baudin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this entry

Cite this entry

Baudin, M., Dutfoy, A., Iooss, B., Popelin, AL. (2017). OpenTURNS: An Industrial Software for Uncertainty Quantification in 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_64

Download citation

Publish with us

Policies and ethics