Abstract
The main goal of this chapter dedicated to catastrophic extreme events is to provide a methodology to better predict their occurrence and better handle them, once they happen. Starting from recent catastrophic extreme events (such as Katerina and Fukushima) in order to illustrate their very high complexity and learn lessons from them, the author shows how combining probability-statistics, modeling, simulation, model reduction, evolution algorithms, dynamical systems, and their control may contribute to the construction of the above, necessarily multi-disciplinary, methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blockley D (1992) Engineering safety. McGraw-Hill, London
Deheuvels P (1991) On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions. Statist Probab Lett 12(5):429–439
Glowinski R, Lions JL, Trémolières R (1976) Analyse numérique des inéquations variationnelles. Dunod, Paris
Kerschen G, Peeters M, Golinval JC, Vakakis AF (2009) Nonlinear normal modes, part I: a useful framework for the structural dynamicist. Mech Syst Sig Process 23(1):170–194
Lions JL (1988) Contrôlabilité exacte, perturbations et stabilisation de systèmes distribués. Masson, Paris
Perrow C (1984) Normal accidents: Living with high-risk technologies. Basic Books, New York
Pidgeon N, Kasperson RE, Slovic P (eds) (2003) The social amplification of risk. Cambridge University Press, Cambridge
Rasmussen J (1997) Risk management in a dynamic society: a modelling problem. Saf Sci 27(2–3):183–213
Stern P, Fineberg H (1996) Understanding risk: informing decisions in a democratic society. National Academies Press, Washington, DC
Thompson JD, Tuden A (1959) Strategies, structures, and processes of organisational decision. In: Thompson JD, Hammond PB, Hawkes RW, Junker BH, Tuden A (eds.) Comparative studies in administration. University of Pittsburgh Press, Pittsburgh, p 195–216
Winter G, Périaux J, Galan M, Cuesta P (eds) (1995) Genetic algorithms in engineering and computer science. Wiley, New York
Acknowledgments
All the Jacques-Louis Lions school’s from US to China, Finland and Japan appear finally to have contributed to that attempt at mastering the risks as part of his great challenge of mastering via computation the great systems so needed to the high technology world of the future. Roland Glowinski continue brilliantly to push ahead the progresses on that way. May I now, however, to finish this presentation, dedicate the thoughts expressed here particularly to Jacques Periaux. Jacques made some quintessential contributions to the numerical analysis of major relevant issues in the simulation of the complexity of our world. His 70th anniversary gives us the opportunity to see together many of the leaders who interacted with him in his distinguished career, so open to new developments in academic and industrial applications. He enabled my Chinese friends and I to recently open a fruitful work in deciphering archeological artifacts in China. We are just now waiting for the Chinese report on the three years’ work carried out diligently by a Franco-Chinese research group, with gratitude for his initial assistance. It is a work that will renew drastically the history of the Eastern Han dynasty and its relations with Middle East. We have here yet another example of excellence in international cooperation, a task in which Jacques was successful many times over and in many countries to help launch and maintain operations as can be seen today by the present audience.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Perrier, P.C. (2014). A Guide for the Selection of a Numerical Methodology Adapted to the Analysis of Extreme Events. In: Fitzgibbon, W., Kuznetsov, Y., Neittaanmäki, P., Pironneau, O. (eds) Modeling, Simulation and Optimization for Science and Technology. Computational Methods in Applied Sciences, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9054-3_9
Download citation
DOI: https://doi.org/10.1007/978-94-017-9054-3_9
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-9053-6
Online ISBN: 978-94-017-9054-3
eBook Packages: EngineeringEngineering (R0)