DATA DRIVEN SCIENCE & ENGINEERING
About the Book
PART I: Dimensionality Reduction and Transforms
Chapter 1: Singular Value Decomposition
Chapter 2: Fourier and Wavelet Transforms
Chapter 3: Sparsity and Compressed Sensing
PART 2: Machine Learning and Data Analysis
Chapter 4: Regression and Model Selection
Chapter 5: Clustering and Classificaiton
Chapter 6: Neural Networks and Deep Learning
PART 3: Dynamics and Control
Chapter 7: Data-Driven Dynamical Systems
Chapter 8: Linear Control Theory
Chapter 9: Balanced Models for Control
Chapter 10: Data-Driven Control
PART 4: Reduced Order Models
Chapter 11: Reduced Order Models
Chapter 12: Interpolation for Parametric Reduced Order Models
Problem Sets
About the Authors
Steven L. Brunton
J. Nathan Kutz
Seminars & Workshops
Physics Informed ML Workshop
Rome Workshop
Machine Learning, Dynamical Systems and Control
Data-Driven Methods for Science and Engineering Seminar
University of Washington,
Seattle