1st Edition

Bayesian Inverse Problems Fundamentals and Engineering Applications

    248 Pages 2 Color & 56 B/W Illustrations
    by CRC Press

    248 Pages 2 Color & 56 B/W Illustrations
    by CRC Press

    248 Pages 2 Color & 56 B/W Illustrations
    by CRC Press

    This book is devoted to a special class of engineering problems called Bayesian inverse problems. These problems comprise not only the probabilistic Bayesian formulation of engineering problems, but also the associated stochastic simulation methods needed to solve them. Through this book, the reader will learn how this class of methods can be useful to rigorously address a range of engineering problems where empirical data and fundamental knowledge come into play. The book is written for a non-expert audience and it is contributed to by many of the most renowned academic experts in this field.

    Part 1: Fundamentals
    1. Introduction to Bayesian Inverse Problems 
    Juan Chiachío-Ruano, Manuel Chiachío-Ruano and Shankar Sankararaman 
    2. Solving Inverse Problems by Approximate Bayesian Computation 
    Manuel Chiachío-Ruano, Juan Chiachío-Ruano and María L. Jalón 
    3. Fundamentals of Sequential System Monitoring and Prognostics Methods 
    David E. Acuña-Ureta, Juan Chiachío-Ruano, Manuel Chiachío-Ruano and Marcos E. Orchard 
    4. Parameter Identification Based on Conditional Expectation 
    Elmar Zander, Noémi Friedman and Hermann G. Matthies 
    Part 2: Engineering Applications 
    5. Sparse Bayesian Learning and its Application in Bayesian System Identification 
    Yong Huang and James L. Beck 
    6. Ultrasonic Guided-waves Based Bayesian Damage Localisation and Optimal Sensor Configuration 
    Sergio Cantero-Chinchilla, Juan Chiachío, Manuel Chiachío and Dimitrios Chronopoulos 
    7. Fast Bayesian Approach for Stochastic Model Updating using Modal Information from Multiple Setups 
    Wang-Ji Yan, Lambros Katafygiotis and Costas Papadimitriou 
    8. A Worked-out Example of Surrogate-based Bayesian Parameter and Field Identification Methods 
    Noémi Friedman, Claudia Zoccarato, Elmar Zander and Hermann G. Matthies 


    Juan Chiachío-Ruano is an Associate Professor of Structural Engineering at University of Granada (Spain), and a researcher at the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). He has devoted his research career to the study and development of Bayesian methods in application to a wide range of Mechanical and Structural Engineering problems. Prior to joining University of Granada, he has developed a significant international research career working at top academic institutions in the UK and the USA.

    Manuel Chiachío-Ruano holds a PhD in Structural Engineering (2014) by the University of Granada (Spain). Currently, he is Associate Professor and Head of the Intelligent Prognostics and Cyber-physical Structural Systems Laboratory (iPHMLab) at the University of Granada. He has developed a significant part of his research in collaboration with the California Institute of Technology (USA), the University of Nottingham (UK) and NASA Ames Research Center (USA), during his stays at these institutions. 

    Shankar Sankararaman received his PhD in Civil Engineering from Vanderbilt University, Nashville, TN, USA, in 2012. Soon after, he joined NASA Ames Research Center, where he developed Machine Learning algorithms and Bayesian methods for system health monitoring, prognostics, decision-making, and uncertainty management. Dr Sankararaman has co-authored a book on prognostics and published over 100 technical articles in international journals and conferences. Presently, Shankar is a scientist at Intuit AI, where he focuses on implementing cutting edge research in products and solutions for Intuit’s customers.