We introduce the use of advanced risk functions, initially conceived in financial engineering, in the formulation of robust optimization problems and illustrate their application to robust design problems of aerodynamic shapes. The focus is here on techniques for increasing the computational efficiency of robust optimization processes based on risk functions. In particular, we illustrate the approximation techniques of empirical probability distributions on which the risk functions are then calculated and discuss the differences in terms of efficiency and easiness of implementation of the intrusive and non-intrusive approximation techniques. Such techniques are then applied to robust aerodynamic design problems.
 E. Morales, A. Bornaccioni, D. Quagliarella and R. Tognaccini, “Gradient based empirical cumulative distribution function approximation for robust aerodynamic design,” Aerospace Science and Technology, 2021, 112(5), n. 106630, (2021), Elsevier BV.
 D. Quagliarella and E. Iuliano, “Robust Design of a Supersonic Natural Laminar Flow Wing-Body”, IEEE Computational Intelligence Magazine, 12(4), 14-27, (2017).
 E. Morales and D. Quagliarella, “Risk Measures in the Context of Robust and Reliability Based Optimization,” in Vasile M. (eds) Optimization Under Uncertainty with Applications to Aerospace Engineering. Springer, Cham, (2021).
 E. Morales, D. Quagliarella and R. Tognaccini, “Gaussian Processes for CVaR Approximation in Robust Aerodynamic Shape Design,” in M. Vasile, D. Quagliarella (eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, Space Technology Proceedings 8, Springer, Cham, (to appear in 2022).
 E. Morales, “Optimal Energy-Driven Aircraft Design Under Uncertainty,” Ph.D. Thesis, Università degli Studi di Napoli “Federico II”, Naples, Italy (2021).
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Zeit: 12:00 Uhr