Название: Bayesian Inverse Problems: Fundamentals and Engineering ApplicationsАвтор: Juan Chiachio-Ruano, Manuel Chiachio-Ruano, Shankar Sankararaman
Издательство: CRC Press
Год: 2022
Страниц: 249
Язык: английский
Формат: pdf (true)
Размер: 11.5 MB
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.
We live in the digital era. As developed societies, we are facing the onset of a new industrial revolution due to the rapid development of technologies including artificial intelligence, internet of things, and soft robotics. As a result, the amount of information and data coming from remotely monitored infrastructures, buildings, vehicles, industrial plants, etc. will increase exponentially over the next few decades. At the same time, our fundamental knowledge about Nature and engineered systems has experienced a rampant increase since the last century, and the computational power available today to process such information has seen a revolutionary transformation. This intersection between empirical (data-driven) and fundamental (physics-based) knowledge has led to the rise of new research topics for knowledge discovery, out of which the Bayesian methods and stochastic simulation are prominent. Just as an example, the increased availability of information coming from digital twins models and the grown ability by intelligent algorithms of fusing such an information with real-time data is leading to intelligent cyber-physical systems such as autonomous cars, smart buildings, etc. This engineering “revolution” enabled by digital technologies and increasing fundamental knowledge is changing the way the 21st century’s engineered assets are designed, built, and operated.
This book is devoted to the so-called “Bayesian methods” and 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. These methods comprise not only the Bayesian formulation of inverse and forward engineering problems, but also the associated stochastic simulation algorithms needed to solve them. All the authors contributing to this book are renowned experts in this field and share the same perception about the importance and relevance of this topic in the upcoming challenges and opportunities brought by the digital revolution in modern engineering.
Contents:
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