Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

by J. Nathan Kutz


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Product Details

ISBN-13: 9780199660339
Publisher: Oxford University Press, USA
Publication date: 09/15/2013
Pages: 608
Product dimensions: 7.50(w) x 9.80(h) x 1.40(d)

About the Author

J. Nathan Kutz, Professor of Applied Mathematics, University of Washington

Professor Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington. Prof. Kutz was awarded the B.S. in physics and mathematics from the University of Washington (Seattle, WA) in 1990 and the PhD in Applied Mathematics from Northwestern University (Evanston, IL) in 1994. He joined the Department of Applied Mathematics, University of Washington in 1998 and became Chair in 2007.

Professor Kutz is especially interested in a unified approach to applied mathematics that includes modeling, computation and analysis. His area of current interest concerns phenomena in complex systems and data analysis (dimensionality reduction, compressive sensing, machine learning), neuroscience (neuro-sensory systems, networks of neurons), and the optical sciences (laser dynamics and modelocking, solitons, pattern formation in nonlinear optics).

Table of Contents

I Basic Computations and Visualization
1. MATLAB Introduction
2. Linear Systems
3. Curve Fitting
4. Numerical Differentiation and Integration
5. Basic Optimization
6. Visualization
II Differential and Partial Differential Equations
7. Initial and Boundary Value Problems of Differential Equations144
8. Finite Difference Methods
9. Time and Space Stepping Schemes: Method of Lines
10. Spectral Methods
11. Finite Element Methods
III Computational Methods for Data Analysis
12. Statistical Methods and Their Applications
13. Time-Frequency Analysis: Fourier Transforms and Wavelets
14. Image Processing and Analysis
15. Linear Algebra and Singular Value Decomposition
16. Independent Component Analysis
17. Image Recognition
18. Basics of Compressed Sensing
19. Dimensionality Reduction for Partial Differential Equations
20. Dynamic Mode Decomposition
21. Data Assimilation Methods
22. Equation Free Modeling
IV Scientific Applications
23. Applications of Differential Equations and Boundary Value Problems
24. Quantum Mechanics
25. Applications of Partial Differential Equations
26. Applications of Data Analysis

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