Data-Driven Modeling & Scientific Computation

John Francis, University of Worcester The book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science

<br>J. Nathan Kutz, Professor of Applied Mathematics, University of Washington <br>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. <br>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). <br>

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