Design and Optimization under Uncertainties A Simulation and Surrogate Model
This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of several of them. A probabilistic optimization requires different kinds of knowledge. First, it is necessary to incorporate the probabilistic behavior into the analysis by estimating how the uncertainties and variations in the model and its parameters are affecting the performance of the system. The focus in this thesis is on sampling based methods to estimate these probabilities. Secondly, an optimization algorithm should be chosen so that the computer can search for and present an optimal solution automatically. Surrogate models can also be used to improve the performances of optimization algorithms when the desire is to optimize computationally expensive objective functions. The author further carried out a comparison of the performance of a set of surrogate models and analyzed how applicable they are on some specific engineering models.