Risk measures in the context of robust and reliability based optimization
Event details
| Date | 17.01.2017 |
| Hour | 15:00 › 17:00 |
| Speaker |
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 Bio: Dr Domenico Quagliarella is Senior Researcher and Head of the Multidisciplinary Analysis and Design Group of Fluid Mechanics Department at the Italia Centre for Aerospace Research (CIRA). He earned on July 1993 a Ph.D. in Aerospace Engineering at University "Federico II" in Naples, Italy, and he got a research engineer position at CIRA in July 1988. His main current research interest is the application of multi-objective optimisation methods to aerodynamic and multidisciplinary design problems, giving particular attention to hybrid optimization techniques such as genetic algorithms coupled with gradient based local search methods. Other fields of active research are approximate fitness evaluators for efficiency improvement of the evolutionary optimization process, and uncertainty incorporation and quantification methods into optimization algorithms for robust and reliability based design. |
| Location | |
| Category | Conferences - Seminars |
Many 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 studied from various points of view related to the treatment of uncertainty.
This talk is related to the use of various risk measures in the context of 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. These risk measures originated in the area of financial engineering, but they are very well and naturally suited to reliability-based design optimization problems and they represent a viable alternative to more traditional robust design approaches.
We will then discuss the implementation of an efficient risk-measure based optimization algorithm based on the introduction of the Weighted Empirical Cumulative Distribution Function (WECDF) and on the use of methods for changing the probability measure.
Finally we will discuss the problems related to the error in the estimation of the risk function and we will illustrate the “bootstrap” computational statistics technique to get an estimate of the standard error on VaR and CVaR. Finally, we will report some application examples of this approach to robust and reliability based optimization, with particular reference to the robust design optimization of a natural laminar flow wing for a supersonic business jet.
This talk is related to the use of various risk measures in the context of 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. These risk measures originated in the area of financial engineering, but they are very well and naturally suited to reliability-based design optimization problems and they represent a viable alternative to more traditional robust design approaches.
We will then discuss the implementation of an efficient risk-measure based optimization algorithm based on the introduction of the Weighted Empirical Cumulative Distribution Function (WECDF) and on the use of methods for changing the probability measure.
Finally we will discuss the problems related to the error in the estimation of the risk function and we will illustrate the “bootstrap” computational statistics technique to get an estimate of the standard error on VaR and CVaR. Finally, we will report some application examples of this approach to robust and reliability based optimization, with particular reference to the robust design optimization of a natural laminar flow wing for a supersonic business jet.
Practical information
- Informed public
- Free
- This event is internal
Organizer
- Penelope Leyland
Contact
- Penelope Leyland