
August 2018 / Approx. viii + 135 pages / Softcover / 9781611975376 / List $59.00 / SIAM Member $41.30 / Order Code: CS19
Keywords: uncertainty quantification, inverse problems, Bayesian methods, computational mathematics, computational statistics
Contents
Preface;
Chapter 1: Introduction;
Chapter 2: The Finite Difference Method;
Chapter 3: The Finite Volume Method;
Chapter 4: The Spectral Method;
Chapter 5: The Finite Element Method;
Bibliography;
Index.
This book is an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The book also presents more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems. Each chapter contains MATLAB® code that implements the algorithms and generates the figures, as well as a large number of exercises accessible to both graduate students and researchers.
Audience
Computational Uncertainty Quantification for Inverse Problems is intended for graduate students, researchers, and applied scientists. It is appropriate for courses on computational inverse problems, Bayesian methods for inverse problems, and UQ methods for inverse problems.
About the Authors
Johnathan M. Bardsley is a professor in the Department of Mathematical Sciences at the University of Montana, where he has been teaching since 2003. He has held longterm visiting professorships at the University of Helsinki, Finland; University of Otago, New Zealand; Technical University of Denmark; and Monash University, Australia, supported by the Gordon Preston Sabbatical Fellowship. Professor Bardsley was a postdoctoral fellow at the NSFfunded Statistical and Applied Mathematical Sciences Institute during its inaugural year in 2002–03. In 2017, he received the Chancellor’s Medallion Award from Montana Tech for excellence in his educational and professional career and for significant contributions to his academic discipline. Professor Bardsley’s research interests focus on inverse problems, uncertainty quantification, computational mathematics, and computational statistics, and he has published many refereed journal articles in these areas.
ISBN 9781611975376