Overview
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.
Full Product Details
Author: Juan Chiachio-Ruano (University of Nottingham, UK) ,
Manuel Chiachio-Ruano (University of Nottingham, UK) ,
Shankar Sankararaman (NASA Ames Research Center, Moffett Field, CA, USA)
Publisher: Taylor & Francis Ltd
Imprint: CRC Press
Weight: 0.460kg
ISBN: 9781032112176
ISBN 10: 1032112174
Pages: 232
Publication Date: 29 January 2024
Audience:
Professional and scholarly
,
College/higher education
,
Professional & Vocational
,
Tertiary & Higher Education
Format: Paperback
Publisher's Status: Active
Availability: In Print
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Author Information
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.
