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Computational Mathematical Modeling An Integrated Approach Across Scales VG Book
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N.º de artículo de eBay:204626999424
Última actualización el 25 jul 2025 01:46:22 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- Book Title
- Computational Mathematical Modeling : An Integrated Approach Acro
- ISBN
- 9781611972474
Acerca de este producto
Product Identifiers
Publisher
Society for Industrial AND Applied Mathematics
ISBN-10
1611972477
ISBN-13
9781611972474
eBay Product ID (ePID)
159873374
Product Key Features
Number of Pages
234 Pages
Publication Name
Computational Mathematical Modeling : an Integrated Approach Across Scales
Language
English
Subject
Differential Equations / General, Probability & Statistics / General, General, Physics / General
Publication Year
2012
Type
Textbook
Subject Area
Mathematics, Science
Series
Mathematical Modeling and Computation Ser.
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
15.8 Oz
Item Length
9 in
Item Width
6 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2012-035528
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
511/.8
Table Of Content
Chapter 1: Review of Multivariate Calculus and Differential Equations Chapter 2: Compartment Models Chapter 3: From Compartment Models to Continuous Models Chapter 4: Dimensional Analysis and Scaling Chapter 5: Introduction to Stochastic Modeling Chapter 6: Modeling of Noise Chapter 7: Modeling with Waiting Processes Chapter 8: Markov Processes Chapter 9: Cellular Automata, Agent Based Modeling
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., Interesting, real-world mathematical modeling 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 modeling paradigms: the macroscopic, in which the authors describe phenomena in terms of time evolution via ordinary differential equations, and the microscopic, which requires knowledge of random events and probability. The text emphasizes the development of computational skills to construct predictive models and analyze the results. To elucidate the concepts, a wealth of examples and portions of MATLAB? code used by the authors are included. Computational Mathematical Modeling: An Integrated Approach Across Scales: Is designed for classroom use, has been extensively tested by the authors, and has homework problems carefully designed to develop students' computational skills. Is based on an unorthodox combination of deterministic and probablistic methodologies that are naturally bridged through examples. Painlessly introduces students to advanced themes in a natural progression. Includes suggestions for further reading., Designed for classroom use, this book has been extensively tested by the authors, and has homework problems carefully designed to develop students' computational skills. It is based on an unorthodox combination of deterministic and probablistic methodologies that are naturally bridged through examples and painlessly introduces students to advanced themes in a natural progression.
LC Classification Number
QA372 .C187 2013
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