Explore Neural ODEs (NODEs), a powerful machine learning approach for learning ODEs from data, in this 25-minute video lecture. Delve into the background of ResNet, understand the transition from ResNet to ODE, and discover why ODEs outperform ResNet. Compare ODE and ResNet performance, and learn about ODE extensions such as Hamiltonian Neural Networks (HNNs) and Lagrangian Neural Networks (LNNs). Gain insights into the ODE algorithm overview, including ODEs and Adjoint Calculation. Produced at the University of Washington with funding support from the Boeing Company, this comprehensive lecture covers essential concepts in Physics Informed Machine Learning.
Overview
Syllabus
Intro
Background: ResNet
From ResNet to ODE
ODE Essential Insight/ Why ODE outperforms ResNet
// ODE Essential Insight Rephrase 1
// ODE Essential Insight Rephrase 2
ODE Performance vs ResNet Performance
ODE extension: HNNs
ODE extension: LNNs
ODE algorithm overview/ ODEs and Adjoint Calculation
Outro
Taught by
Steve Brunton
