Uncertainty Modeling for Engineering Applications

This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop Uncertainty Modeling for Engineering Applications (UMEMA 2017), held in Torino, Italy in November 2017.

Shares cutting edge uncertainty quantification ideas with a wide audience Draws together the work of engineers, mathematicians, physicists, and statisticians Provides a view of a traditionally mathematical topics from an engineering perspective

Auteur

Flavio G. Canavero is professor of Circuit Theory and Electromagnetic Compatibility at Politecnico di Torino, Italy. His research interests include: signal integrity and electromagnetic compatibility, modeling of lossy and non-uniform interconnects, circuit equivalents via model order reduction, behavioral modeling of digital devices, field coupling to multiwire cables, statistical methods in EMC, interaction of electromagnetic radiation with biological systems, remote sensing of the atmosphere.

Contenu
Quadrature Strategies for Constructing Polynomial Approximations.- Weighted reduced order methods for parametrized partial differential equations with random inputs.- A new approach for state estimation.- Data-efficient Sensitivity Analysis with Surrogate Modeling.- Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival based Localization.- Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate.- Stochastic Dosimetry for Radio-Frequency exposure assessment in realistic scenarios.- On the Various Applications of Stochastic Collocation in Computational Electromagnetics.- Reducing the statistical complexity of EMC testing: improvements for radiated experiments using stochastic collocation and bootstrap methods.- Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models.- Measurement uncertainty cannot always be calculated.