Librería Portfolio

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

Provides in-depth examples paired with comprehensive, open-source code
Features concise, digestible explanations of complex concepts and their applications
Includes extensive online supplements with homeworks, case studies, and supplementary code



Table of Contents
Part I. Dimensionality Reduction and Transforms:
1. Singular value decomposition
2. Fourier and wavelet transforms
3. Sparsity and compressed sensing
Part II. Machine Learning and Data Analysis:
4. Regression and model selection
5. Clustering and classification
6. Neural networks and deep learning
Part III. Dynamics and Control:
7. Data-driven dynamical systems
8. Linear control theory
9. Balanced models for control
10. Data-driven control
Part IV. Reduced-Order Models:
11. Reduced-order models (ROMs)
12. Interpolation for parametric ROMs.