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