Fractional Differential Equations: An Approach via Fractional Derivatives (Applied Mathematical Sciences) - Softcover

Jin, Bangti

Softcover

ISBN 10: 3030760456 ISBN 13: 9783030760458

Publisher: Springer, 2022

Synopsis

This graduate textbook provides a self-contained introduction to modern mathematical theory on fractional differential equations. It addresses both ordinary and partial differential equations with a focus on detailed solution theory, especially regularity theory under realistic assumptions on the problem data. The text includes an extensive bibliography, application-driven modeling, extensive exercises, and graphic illustrations throughout to complement its comprehensive presentation of the field. It is recommended for graduate students and researchers in applied and computational mathematics, particularly applied analysis, numerical analysis and inverse problems.

About this title

About the Author

Bangti Jin received the B.Eng. degree in polymeric materials and engineering in 2002, the

M.Sc. degree in computational mathematics in 2005, both from Zhejiang University, Hangzhou, China, and the Ph.D. degree in applied mathematics from the Chinese University of Hong Kong, Hong Kong, in 2008. Previously, he was an Assistant Professor of mathematics at the University of California, Riverside (2013–2014), a Visiting Assistant Professor at Texas A&M University (2010–2013), an Alexandre von Humboldt Postdoctoral Researcher at the University of Bremen (2009–2010). He is currently Professor of Inverse Problems at the Department of Computer Science, University College London, London, U.K. His research interests include computational inverse problems and numerical analysis of differential equations.

From the Back Cover

This graduate textbook provides a self-contained introduction to modern mathematical theory on fractional differential equations. It addresses both ordinary and partial differential equations with a focus on detailed solution theory, especially regularity theory under realistic assumptions on the problem data. The text includes an extensive bibliography, application-driven modeling, extensive exercises, and graphic illustrations throughout to complement its comprehensive presentation of the field. It is recommended for graduate students and researchers in applied and computational mathematics, particularly applied analysis, numerical analysis and inverse problems.

Edition details

• Publisher: Springer
• Publication date: 2022
• Language: English
• ISBN 10: 3030760456
• ISBN 13: 9783030760458
• Binding: Paperback
• Edition number: 1
• Number of pages: 384