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SUMMARY:Risk measures in the context of robust and reliability based optim
 ization
DTSTART:20170117T150000
DTEND:20170117T170000
DTSTAMP:20260429T215840Z
UID:a0a8db4ff538af6d5f52fd0f25206be0f94cb974211180b1b05913e7
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Domenico Quagliarella\, Senior Researcher and Head of the 
 Multidisciplinary Analysis and Design Group\, Fluid Mechanics Department\,
  C.I.R.A. - Italian Aerospace Research Centre\n\nBio: Dr Domenico Quagliar
 ella is Senior Researcher and Head of the Multidisciplinary Analysis and D
 esign Group of Fluid Mechanics Department at the Italia Centre for Aerospa
 ce Research (CIRA). He earned on July 1993 a Ph.D. in Aerospace Engineerin
 g at University "Federico II" in Naples\, Italy\, and he got a research en
 gineer position at CIRA in July 1988. His main current research interest i
 s the application of multi-objective optimisation methods to aerodynamic a
 nd multidisciplinary design problems\, giving particular attention to hybr
 id optimization techniques such as genetic algorithms coupled with gradien
 t based local search methods. Other fields of active research are approxim
 ate fitness evaluators for efficiency improvement of the evolutionary opti
 mization process\, and uncertainty incorporation and quantification method
 s into optimization algorithms for robust and reliability based design.\nM
 any industrial optimization processes must take account of the stochastic 
 nature of the system and processes to be designed or re-designed and have 
 to consider the random variability of some of the parameters that describe
  them. Thus it is necessary to characterize the system that is being studi
 ed from various points of view related to the treatment of uncertainty.\n\
 nThis talk is related to the use of various risk measures in the context o
 f robust and reliability based optimization. We start from the definition 
 of risk measure and its formal setting and then we show how different risk
  functional definitions can lead to different approaches to the problem of
  optimization under uncertainty. In particular\, the application of value-
 at-risk (VaR) and conditional value-at-risk (CVaR) is here illustrated. Th
 ese risk measures originated in the area of financial engineering\, but th
 ey are very well and naturally suited to reliability-based design optimiza
 tion problems and they represent a viable alternative to more traditional 
 robust design approaches.\n\nWe will then discuss the implementation of an
  efficient risk-measure based optimization algorithm based on the introduc
 tion of the Weighted Empirical Cumulative Distribution Function (WECDF) an
 d on the use of methods for changing the probability measure.\n\nFinally w
 e will discuss the problems related to the error in the estimation of the 
 risk function and we will illustrate the “bootstrap” computational sta
 tistics technique to get an estimate of the standard error on VaR and CVaR
 . Finally\, we will report some application examples of this approach to r
 obust and reliability based optimization\, with particular reference to th
 e robust design optimization of a natural laminar flow wing for a superson
 ic business jet.\n 
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