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Computational Mathematical Modeling: An Integrated Approach Across Scales [Pehme köide]

  • Formaat: Paperback / softback, 234 pages, kõrgus x laius x paksus: 229x152x12 mm, kaal: 447 g, illustrations
  • Sari: Mathematical Modeling and Computation 17
  • Ilmumisaeg: 30-Dec-2012
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 1611972477
  • ISBN-13: 9781611972474
Teised raamatud teemal:
  • Formaat: Paperback / softback, 234 pages, kõrgus x laius x paksus: 229x152x12 mm, kaal: 447 g, illustrations
  • Sari: Mathematical Modeling and Computation 17
  • Ilmumisaeg: 30-Dec-2012
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 1611972477
  • ISBN-13: 9781611972474
Teised raamatud teemal:
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.
Preface ix
1 Review of multivariate calculus and differential equations 1(30)
1.1 Differentiability, linearization
1(2)
1.2 Ordinary differential equations
3(7)
1.3 Numerical methods for solving ordinary differential equations
10(19)
1.4 Notes and comments
29(1)
Exercises
30(1)
2 Compartment models 31(32)
2.1 Simple compartment models
32(11)
2.2 Interacting population models
43(8)
2.3 Subcompartment models
51(5)
2.4 Notes and comments
56(4)
Exercises
60(3)
3 From compartment models to continuous models 63(20)
3.1 Refining compartment partitioning
63(7)
3.2 Age-structured models: From discrete to continuous
70(9)
3.3 Notes and comments
79(1)
Exercises
80(3)
4 Dimensional analysis and scaling 83(26)
4.1 Model scaling: A preliminary example
83(4)
4.2 Dimensional analysis
87(16)
4.3 Refining the models: Expansion parameters
103(2)
4.4 Notes and comments
105(2)
Exercises
107(2)
5 Introduction to stochastic modeling 109(24)
5.1 Random variables, distributions, and densities
111(12)
5.2 Sampling and histograms
123(7)
5.3 Notes and comments
130(1)
Exercises
131(2)
6 Modeling noise 133(26)
6.1 Counting processes and Poisson noise
141(5)
6.2 Modeling multichannel noise
146(2)
6.3 Temporal analysis: Autocovariance function
148(5)
6.4 Spatiotemporal noise and Kronecker products
153(2)
6.5 Notes and comments
155(1)
Exercises
155(4)
7 Modeling with waiting processes 159(24)
7.1 Neuron firing and biological noise
159(8)
7.2 Stochastic simulation of chemical kinetics
167(9)
7.3 Photon migration and Levy flight
176(5)
7.4 Notes and comments
181(1)
Exercises
182(1)
8 Markov processes 183(16)
8.1 Markov processes and random walks
183(7)
8.2 Stochastic predator-prey model
190(7)
8.3 Notes and comments
197(1)
Exercises
197(2)
9 Cellular automata, agent-based modeling 199(16)
9.1 Markov processes: Temporal and spatial
199(12)
9.2 Notes and comments
211(1)
Exercises
212(3)
Bibliography 215(4)
Index 219
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.