Computational Mathematical Modeling: An Integrated Approach Across Scales

Calvetti, Daniela; Somersalo, Erkki

ISBN 10: 1611972477 / ISBN 13: 9781611972474

Published by Society for Industrial & Applied Mathematics,U.S., 2013

About this Item

2012. Paperback. A textbook that covers mathematical modeling at a range of scales from a computational viewpoint, suitable for advanced undergraduates. Series: Monographs on Mathematical Modeling & Computation. Num Pages: 234 pages, illustrations. BIC Classification: PBWH. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 177 x 13. Weight in Grams: 436. . . . . . Books ship from the US and Ireland.

Seller Inventory # V9781611972474

Bibliographic Details

• Title: Computational Mathematical Modeling: An Integrated Approach Across Scales
• Publisher: Society for Industrial & Applied Mathematics,U.S.
• Publication Date: 2013
• Language: English
• ISBN 10: 1611972477
• ISBN 13: 9781611972474
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Interesting real-world mathematical modelling problems are complex and can usually be studied at different scales. The scale at which the investigation is carried out is one of the factors that determines the type of mathematics most appropriate to describe the problem. The book concentrates on two modelling paradigms: the macroscopic, in which phenomena are described in terms of time evolution via ordinary differential equations; and the microscopic, which requires knowledge of random events and probability. The exposition is based on this unorthodox combination of deterministic and probabilistic methodologies, and emphasizes the development of computational skills to construct predictive models. To elucidate the concepts, a wealth of examples, self-study problems, and portions of MATLAB code used by the authors are included. This book, which has been extensively tested by the authors for classroom use, is intended for students in mathematics and the physical sciences at the advanced undergraduate level and above.

About the Authors

Daniela Calvetti is a Professor of Mathematics at Case Western Reserve University. Her research interests include numerical linear algebra, large scale scientific computing, Bayesian statistical computing and modeling. She has published over 100 research papers and one monograph.

Erkki Somersalo is a Professor at Case Western Reserve University. His areas of expertise are computational inverse problems, statistical scientific computing, and fields and waves, in particular with applications to medical imaging. His work also includes applications in life sciences and medicine. He has published two monographs and over a hundred research papers.