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
Deep Learning in Fluid Mechanics
Course Materials
Machine Learning in Fluids
Overview of Fluid Mechanics
Deep Learning Introduction
Turbulence Closures
Time-Stepping and Flow Maps
Dimensionality Reduction
Spatio-Temporal Systems
Flow Control: Reinforcement Learning
Flow Control: Model Predictive Control
Research Abstracts
Machine Learning, Dynamical Systems and Control
Turbulence Models: RANS and LES
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Video
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Deep Turbulence Closure Modeling
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Video
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Reynolds Averaged Navier Stokes (RANS) Part 1
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Video
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Reynolds Averaged Navier Stokes (RANS) Part 2
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Video
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