Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Dynamical Systems

Steve Brunton via YouTube

Overview

Prepare for a new career with $100 off Coursera Plus
Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.
This course on Dynamical Systems aims to teach students the following learning outcomes and goals: understanding Sparse Identification of Nonlinear Dynamics (SINDy), Koopman Observable Subspaces, Finite Linear Representations of Nonlinear Dynamics for Control, Koopman Operator Optimal Control, Compressed Sensing, Dynamic Mode Decomposition, Hankel Alternative View of Koopman (HAVOK) Analysis, Magnetic field reversal, Measles outbreaks, simulating chaotic systems in Matlab, Discrete-Time Dynamical Systems, Deep Learning of Dynamics and Coordinates with SINDy Autoencoders, Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows, SINDy-PI algorithm, and Deep Delay Autoencoders. The course teaches skills such as data analysis, simulation in Matlab, deep learning, and algorithm implementation. The teaching method includes lectures, hands-on simulations, and practical exercises. This course is intended for students and professionals interested in dynamical systems, nonlinear dynamics, control theory, data analysis, and deep learning.

Syllabus

Sparse Identification of Nonlinear Dynamics (SINDy).
Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control.
Koopman Observable Subspaces & Nonlinearization.
Koopman Operator Optimal Control.
Compressed Sensing and Dynamic Mode Decomposition.
Hankel Alternative View of Koopman (HAVOK) Analysis [FULL].
Hankel Alternative View of Koopman (HAVOK) Analysis [SHORT].
Magnetic field reversal and Measles outbreaks: HAVOK models of chaos.
Linear model for chaotic Lorenz system [HAVOK].
Simulating the Lorenz System in Matlab.
Discrete-Time Dynamical Systems.
Simulating the Logistic Map in Matlab.
The Anatomy of a Dynamical System.
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders.
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders.
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders.
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows.
SINDy-PI: A robust algorithm for parallel implicit sparse identification of nonlinear dynamics.
Deep Delay Autoencoders Discover Dynamical Systems w Latent Variables: Deep Learning meets Dynamics!.

Taught by

Steve Brunton

Reviews

Start your review of Dynamical Systems

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.