Uploaded December 2022 | Updated September 2026, 2 weeks ago
In this video, we code up the Forward Euler and Backward Euler integration schemes in Python and Matlab, investigating stability and error as a function of the time step. We test these integrators on the simple spring-mass-damper system, where we have an analytic solution to compare against.
Playlist: youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
Course Website: http://faculty.washington.edu/sbrunton/me564/
Codes (Python): http://faculty.washington.edu/sbrunton/me564/python/L17_pend.ipynb and http://faculty.washington.edu/sbrunton/me564/python/L17_simpend.ipynb
Codes (Matlab): http://faculty.washington.edu/sbrunton/me564/matlab/L17_pend.m and http://faculty.washington.edu/sbrunton/me564/matlab/L17_simpend.m
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Problem setup
8:39 Matlab code example
20:20 Python code example
In this video, we code up the Forward Euler and Backward Euler integration schemes in Python and Matlab, investigating stability and error as a function of the time step. We test these integrators on the simple spring-mass-damper system, where we have an analytic solution to compare against.
Playlist: youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
Course Website: http://faculty.washington.edu/sbrunton/me564/
Codes (Python): http://faculty.washington.edu/sbrunton/me564/python/L17_pend.ipynb and http://faculty.washington.edu/sbrunton/me564/python/L17_simpend.ipynb
Codes (Matlab): http://faculty.washington.edu/sbrunton/me564/matlab/L17_pend.m and http://faculty.washington.edu/sbrunton/me564/matlab/L17_simpend.m
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Problem setup
8:39 Matlab code example
20:20 Python code example





![Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model
This video gives an intro example of how we model complex systems that change in time, using matrices and vectors. Specifically, I build a toy model for the weather, where the probability of the weather today being (R)ainy, (N)ice, or (C)loudy is stored in a vector [R, N, C]. This probability of the weather being in one of these states tomorrow is then updated by multiplying this vector by a probability matrix.
Code examples are given in Python and Matlab.
Playlist: https://www.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
1:07 Building a simple weather model
5:00 Modeling the state as a vector
6:50 Writing the dynamical system update rule as a matrix
14:07 Matlab code example
23:43 Python code example
38:24 Teaser of how to make system more realistic Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model](https://i.ytimg.com/vi/K-8F_zDMDUI/mqdefault.jpg)




![New Advances in Artificial Intelligence and Machine Learning
[Tier 1, Lecture 3] This video describes modern advances in machine learning and artificial intelligence, which are rapidly evolving technologies. Topics include generative AI (diffusion models, DALL-E 2, ChatGPT, etc.), reinforcement learning, computer vision, etc.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
0:00 Overview
1:04 Image Classification
3:14 The Importance of Training Data
7:19 Generative Images
8:22 Image Captioning
9:18 DALL-E 2
11:08 History of Deep Dream
14:15 Text Generation and NLP
16:24 ChatGPT and LLMs
18:55 What is ML good at?
20:36 Reinforcement Learning and Atari
24:45 Chaos and Weather
26:45 Outro New Advances in Artificial Intelligence and Machine Learning](https://i.ytimg.com/vi/NQkSH_CBPq8/mqdefault.jpg)