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Data-driven nonlinear aeroelastic models of morphing wings for control
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Classroom Contents
Data-Driven Dynamical Systems with Machine Learning
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- 1 Data-Driven Dynamical Systems Overview
- 2 The Anatomy of a Dynamical System
- 3 Simulating the Lorenz System in Matlab
- 4 Discrete-Time Dynamical Systems
- 5 Simulating the Logistic Map in Matlab
- 6 Dynamic Mode Decomposition (Overview)
- 7 Dynamic Mode Decomposition (Examples)
- 8 Dynamic Mode Decomposition (Code)
- 9 Compressed Sensing and Dynamic Mode Decomposition
- 10 Sparse Identification of Nonlinear Dynamics (SINDy)
- 11 Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
- 12 Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models
- 13 Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models
- 14 Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities
- 15 Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms
- 16 PySINDy: A Python Library for Model Discovery
- 17 PDE FIND
- 18 Koopman Spectral Analysis (Overview)
- 19 Koopman Spectral Analysis (Representations)
- 20 Koopman Spectral Analysis (Control)
- 21 Koopman Spectral Analysis (Continuous Spectrum)
- 22 Koopman Spectral Analysis (Multiscale systems)
- 23 Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control
- 24 Hankel Alternative View of Koopman (HAVOK) Analysis [FULL]
- 25 Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
- 26 Data-driven Modeling of Traveling Waves
- 27 Machine Learning for Fluid Mechanics
- 28 Data-Driven Resolvent Analysis
- 29 Data-driven nonlinear aeroelastic models of morphing wings for control
- 30 Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
- 31 Interpretable Deep Learning for New Physics Discovery
- 32 Kernel Learning for Robust Dynamic Mode Decomposition
- 33 A high level view of reduced order modeling for plasmas
- 34 Promoting global stability in data-driven models of quadratic nonlinear dynamics - Trapping SINDy
- 35 Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
- 36 Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization
- 37 SINDy-PI: A robust algorithm for parallel implicit sparse identification of nonlinear dynamics
- 38 Reinforcement Learning Series: Overview of Methods
- 39 Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming
- 40 Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
- 41 Overview of Deep Reinforcement Learning Methods
- 42 Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming
- 43 Deep Delay Autoencoders Discover Dynamical Systems w Latent Variables: Deep Learning meets Dynamics!
