Data-Driven Dynamical Systems with Machine Learning

Data-Driven Dynamical Systems with Machine Learning

Steve Brunton via YouTube Direct link

Data-Driven Dynamical Systems Overview

1 of 43

1 of 43

Data-Driven Dynamical Systems Overview

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Data-Driven Dynamical Systems with Machine Learning

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

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