Uploaded October 2022 | Updated September 2026, 2 weeks ago
This video describes how to analyze fully nonlinear differential equations by analyzing the linearized dynamics near a fixed point. Most of our powerful solution techniques for ODEs are only valid for linear systems, so this is an important strategy for studying nonlinear systems.
This is a hugely important step towards analyzing nonlinear systems with linear techniques.
Playlist: youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
Course Website: http://faculty.washington.edu/sbrunton/me564/
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Overview
3:37 Fixed points of nonlinear systems
5:32 Zooming in to small neighborhood of fixed point
7:03 Solving for linearization with Taylor series
12:10 Computing Jacobian matrix of partial derivatives
15:10 Example of linearizing nonlinear system
This video describes how to analyze fully nonlinear differential equations by analyzing the linearized dynamics near a fixed point. Most of our powerful solution techniques for ODEs are only valid for linear systems, so this is an important strategy for studying nonlinear systems.
This is a hugely important step towards analyzing nonlinear systems with linear techniques.
Playlist: youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
Course Website: http://faculty.washington.edu/sbrunton/me564/
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Overview
3:37 Fixed points of nonlinear systems
5:32 Zooming in to small neighborhood of fixed point
7:03 Solving for linearization with Taylor series
12:10 Computing Jacobian matrix of partial derivatives
15:10 Example of linearizing nonlinear system
![AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]](https://i.ytimg.com/vi/T4iJ10TAIMg/mqdefault.jpg "AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]
This video discusses the fifth stage of the machine learning process: (5) selecting and implementing an optimization algorithm to train the model. There are opportunities to incorporate physics into this stage of the process, such as using constrained optimization to force a model onto a susbpace or submanifold characterized by a symmetry or other physical constraint.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:45 Case Study: KKT Constrained Least Squares
06:18 Case Study: Physics Informed DMD
14:00 Loss vs Optimization of Subspace Constraints
17:50 Subspace Constraints and Symmetry
19:28 Case Study: Symbolic Regression and Evolutionary Optimization
22:25 Parsimony and Sparse Optimization Algorithms
25:03 Case Study: SINDy and SR3
28:38 Parsimony and Sparsity Hyperparameters
30:55 Outro")
