Engineering structures, dynamic systems and mechanical components are affected by different types of stochastic uncertainties. Stochastic Optimization provides a well-suited framework to determine optimal designs or controls coping with such uncertainties. Hence, the original control or optimization problem with unknown, random varying parameters must be replaced by an appropriate deterministic substitute problem incorporating the available a priori and sample information about the random parameter variations. Using appropriate loss functions, substitute problems demand then the minimization and/or restriction of expected costs.
The Special Session focuses on contributions dealing with the definition and mathematical analysis of deterministic substitute problems, and/or treating approximation concepts, simulation techniques, analytical and numerical methods to solve deterministic substitute problems. Moreover, applications to several concrete fields such as optimal structural design, feedback control, open-loop feedback control, nonlinear model prediction control under stochastic uncertainty are welcome.