Uploaded April 2023 | Updated September 2026, 2 weeks ago
This video presents examples of how to use the various complex integration theorems to compute challenging complex integrals.
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
This video presents examples of how to use the various complex integration theorems to compute challenging complex integrals.
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington






![Measure-preserving EDMD: A 4-line structure-preserving & convergent DMD algorithm!
Research Abstract by Matt Colbrook, Cambridge University
We introduce measure-preserving extended dynamic mode decomposition (mpEDMD), a data-driven algorithm that enforces measure-preserving truncations of Koopman operators using a general dictionary of observables. It is flexible and easy to use with any pre-existing DMD-type method and with different data types. As well as convergence to the spectral properties of the underlying Koopman operator (for general measure-preserving dynamical systems), mpEDMD has improved stability and qualitative behavior of trajectories. For delay embedding, mpEDMD even comes with explicit convergence rates as the size of the dictionary increases. We demonstrate mpEDMD on a range of challenging examples, its increased robustness to noise compared with other DMD-type methods, and its ability to capture the energy conservation and statistics of a turbulent boundary layer flow with Reynolds number greater than 60,000 and state-space dimension greater than 100,000.
http://www.damtp.cam.ac.uk/user/mjc249/pdfs/mpEDMD.pdf [damtp.cam.ac.uk] Measure-preserving EDMD: A 4-line structure-preserving & convergent DMD algorithm!](https://i.ytimg.com/vi/Xt3vS_hhBm8/mqdefault.jpg)



![A Neural Network Primer
[Tier 1, Lecture 04c] This video provides a primer on neural networks for machine learning and artificial intelligence. Neural networks are biologically inspired and provide the backbone of many modern ML/AI frameworks.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
0:00 Overview
2:15 What is a Neural Network?
5:17 The Perceptron (History of Neural Networks)
6:39 Deep Learning
8:50 A Diversity of Architectures: the Neural Network Zoo
11:30 CNN: Convolutional Neural Networks
13:11 RNN: Recurrent Neural Networks
14:01 Autoencoder Networks
16:20 Outro A Neural Network Primer](https://i.ytimg.com/vi/_56bfCu02ZE/mqdefault.jpg)