Skip to content
Back Home
  • Home
  • The Chair
    • About
    • Our Head
    • Our Team
    • Past Members
    • Contact
  • Research
    • Publications
    • Projects
    • Teaching
    • Initiatives
    • Posts on Math and Research
    • Contributors
  • Join us!
    • Jobs board
    • Upcoming events
    • Past Events
  • Resources
    • Seminars
    • Math to go!
    • Akademy
    • GitHub
  • Search
Back Home
  • Search
  • Home
  • The Chair
    • About
    • Our Head
    • Our Team
    • Past Members
    • Contact
  • Research
    • Publications
    • Projects
    • Teaching
    • Initiatives
    • Posts on Math and Research
    • Contributors
  • Join us!
    • Jobs board
    • Upcoming events
    • Past Events
  • Resources
    • Seminars
    • Math to go!
    • Akademy
    • GitHub
Home » Hub » Training of neural ODEs using pyTorch

Training of neural ODEs using pyTorch

Published September 13, 2022

Start with tutorials to get familiar with the code
Tutorial 1: Train a neural ODE based network on point cloud data set and generating a gif of the resulting time evolution of the neural ODE

Code:

Code is based on GitHub: borjanG : 2021-dynamical-systems that uses the torchdiffeq package GitHub : rtqichen: torchdiffeq

 

Code:

|| Go to the Math & Research main page

You may also like

Published December 16, 2021

Hamilton-Jacobi Equations: Inverse Design

Author: Carlos Esteve, Deusto CCM Code: In a previous post “Inverse Design For Hamilton-Jacobi Equations“, described all the possible initial states that […]

Published December 9, 2021

Augmented Lagragian preconditioners for incompressible flow

Author: Alexei Gazca, FAU DCN-AvH Code:   Below is a description of the types of problems that can be tackled using the […]

Published December 14, 2021

Random Batch Methods for Linear-Quadratic Optimal Control Problems

Author: Daniel Veldman, FAU DCN-AvH Code: || Also available @Daniël’s GitHub In a previous post “Randomized time-splitting in linear-quadratic optimal control“, it […]

Published January 3, 2022

pyGasControls library (simulation software)

Author: Martin Gugat, Enrique Zuazua, Aleksey Sikstel, FAU DCN-AvH Code:   [HINT] To run the software on your computer, you may have […]

Post navigation

  • Previous post Course: A Practical Introduction to Control, Numerics, and Machine Learning (IFAC CPDE 2022)
  • Back to post list
  • Next post FAU MoD Lecture: Learning-Based Optimization and PDE Control in User-Assignable Finite Time
Our lastest
  • Shape optimization problems in thermal insulation with thin insulating layers
  • Parabolic trajectories and the Harnack inequality
  • A Deep Learning approach to Reduced Order Modeling of Parameter dependent Partial Differential Equations

©  2019 - 2023  – All rights reserved - FAU DCN-AvH Chair for Dynamics, Control and Numerics - Alexander von Humboldt Professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany Imprint | Privacy | Accessibility | Contact