Optimization Under Uncertainty with Applications to Aerospace Engineering
Springer International Publishing (Verlag)
978-3-030-60165-2 (ISBN)
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.
lt;b>Massimiliano Vasile is a Professor of Space Systems Engineering and Director of the Aerospace Centre of Excellence in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde. Previous to this, he was a Senior Lecturer in the Department of Aerospace Engineering and Head of Research for the Space Advanced Research Team at the University of Glasgow. Before starting his academic career in 2004, he was the first member of the European Space Agency (ESA) Advanced Concepts Team (ACT) and initiator of the ACT research stream on global trajectory optimization, mission analysis and biomimicry. His research interests include Computational Optimization, Robust Multi-Disciplinary Design and Optimization Under Uncertainty.
More recently, Professor Vasile has undertaken extensive research on techniques for asteroid manipulation as well as the sustainability of the space environment. His research has been funded by the European Space Agency, EPSRC, the Planetary Society and the European Commission. He was the Lead of the Stardust Marie Curie ITN on debris and asteroid manipulation and is now leading the UTOPIAE (Uncertainty Treatment and Optimization In Aerospace Engineering) Marie Curie ETN on optimization under uncertainty and uncertainty quantification in complex aerospace engineering systems.
- 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.
| Erscheinungsdatum | 01.03.2021 |
|---|---|
| Zusatzinfo | VI, 573 p. 155 illus., 108 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1027 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Naturwissenschaften ► Physik / Astronomie ► Astronomie / Astrophysik | |
| Schlagworte | Aerospace Engineering • Bayesian techniques • Evidence Theory for Robust Optimization • Imprecise Markov Chains • Imprecise Probabilities • Resilience in Complex Engineering Systems • uncertainty quantification |
| ISBN-10 | 3-030-60165-X / 303060165X |
| ISBN-13 | 978-3-030-60165-2 / 9783030601652 |
| Zustand | Neuware |
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