Bayesian Inverse Problems: Fundamentals and Engineering Applications

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.

本書專注於一類特殊的工程問題，稱為「貝葉斯反問題」。這些問題不僅包括工程問題的概率貝葉斯形式，還包括解決這些問題所需的相關隨機模擬方法。通過本書，讀者將學習到這類方法如何在處理涉及實證數據和基礎知識的各種工程問題時具有嚴謹性。本書針對非專業讀者撰寫，並由該領域中許多最著名的學術專家共同貢獻。

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.

Juan Chiachío-Ruano是西班牙格拉納達大學結構工程學副教授，也是安達魯西亞數據科學和計算智能研究所（DaSCI）的研究員。他專注於研究和開發貝葉斯方法，應用於各種機械和結構工程問題。在加入格拉納達大學之前，他在英國和美國的頂尖學術機構工作，建立了重要的國際研究生涯。

Manuel Chiachío-Ruano於2014年獲得西班牙格拉納達大學的結構工程博士學位。目前，他是格拉納達大學智能預測和物理結構系統實驗室（iPHMLab）的副教授和負責人。他的研究工作與美國加州理工學院、英國諾丁漢大學和美國NASA艾姆斯研究中心合作完成。

Shankar Sankararaman於2012年在美國田納西州納什維爾的范德比爾特大學獲得土木工程博士學位。隨後，他加入了NASA艾姆斯研究中心，在那裡開發了機器學習算法和貝葉斯方法，用於系統健康監測、預測、決策和不確定性管理。Sankararaman博士合著了一本關於預測的書籍，並在國際期刊和會議上發表了100多篇技術文章。目前，Shankar是Intuit AI的科學家，致力於將尖端研究應用於Intuit的產品和解決方案中，以滿足客戶的需求。