Bayesian Inverse Problems: Fundamentals and Engineering Applications [Paperback]

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.

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 2. Solving Inverse Problems by Approximate Bayesian Computation 3. Fundamentals of Sequential System Monitoring and Prognostics Methods 4. Parameter Identification Based on Conditional Expectation Part 2 Engineering Applications 5. Sparse Bayesian Learning and its Application in Bayesian System Identification 6. Ultrasonic Guided-waves Based Bayesian Damage Localisation and Optimal Sensor Configuration 7. Fast Bayesian Approach for Stochastic Model Updating using Modal Information from Multiple Setups 8. A Worked-out Example of Surrogate-based Bayesian Parameter and Field Identification Methods

Juan Chiach?o-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 develƒ3