About Course
Data-Driven Dynamical Systems with Machine Learning.
Data-Driven Dynamical 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
- Online supplements include homework, video lectures, and code and datasets in MATLAB® and Python.
Course Content
Data-Driven Dynamical Systems with Machine Learning
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Data-Driven Dynamical Systems Overview
21:43 -
Simulating the Lorenz System in Matlab
00:00 -
Discrete-Time Dynamical Systems
09:46 -
Simulating the Logistic Map in Matlab
16:29 -
Hankel Alternative View of Koopman (HAVOK) Analysis [FULL]
00:00 -
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
24:31 -
PySINDy: A Python Library for Model Discovery
12:16 -
Data-driven Modeling of Traveling Waves
00:00 -
Machine Learning for Fluid Mechanics
00:00 -
Data-Driven Resolvent Analysis
00:00 -
Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control
00:00 -
Koopman Spectral Analysis (Multiscale systems)
00:00 -
Dynamic Mode Decomposition (Overview)
18:18 -
Dynamic Mode Decomposition (Examples)
07:43 -
Dynamic Mode Decomposition (Code)
08:14 -
Compressed Sensing and Dynamic Mode Decomposition
00:00 -
Sparse Identification of Nonlinear Dynamics (SINDy)
26:44 -
Koopman Spectral Analysis (Overview)
27:49 -
Koopman Spectral Analysis (Representations)
16:01 -
Koopman Spectral Analysis (Control)
00:00 -
Koopman Spectral Analysis (Continuous Spectrum)
00:00 -
Data-driven nonlinear aeroelastic models of morphing wings for control
21:10
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