Full Description
In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering.
The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.
Contents
- Introduction to Spectral Methods for Uncertainty Quantification. - Introduction to Imprecise Probabilities. - Uncertainty Quantification in Lasso-Type Regularization Problems. - Reliability Theory. - An Introduction to Imprecise Markov Chains. - Fundamentals of Filtering. - Introduction to Optimisation. - An Introduction to Many-Objective Evolutionary Optimization. - Multilevel Optimisation. - Sequential Parameter Optimization for Mixed-Discrete Problems. - Parameter Control in Evolutionary Optimisation. - Response Surface Methodology. - Risk Measures in the Context of Robust and Reliability Based Optimization. - Best Practices for Surrogate Based Uncertainty Quantification in Aerodynamics and Application to Robust Shape Optimization. - In-flight Icing: Modeling, Prediction, and Uncertainty. - Uncertainty Treatment Applications: High-Enthalpy Flow Ground Testing. - Introduction to Evidence-Based Robust Optimisation.
