Skip to content
Back Home
  • Home
  • The Chair
    • About
    • Our Head
    • Our Team
    • Contact
    • Past Members
  • Research
    • Publications
    • Projects
    • Teaching
    • Initiatives
    • Posts on Math and Research
    • Contributors
  • Join us!
    • Careers
    • Events
    • Past Events
  • Resources
    • Seminars / Lectures
    • Math to go!
    • Academy
    • GitHub
  • Search
Back Home
  • Search
  • Home
  • The Chair
    • About
    • Our Head
    • Our Team
    • Contact
    • Past Members
  • Research
    • Publications
    • Projects
    • Teaching
    • Initiatives
    • Posts on Math and Research
    • Contributors
  • Join us!
    • Careers
    • Events
    • Past Events
  • Resources
    • Seminars / Lectures
    • Math to go!
    • Academy
    • GitHub

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 1, 2023

PINNs Introductory Code for the Heat Equation

PINNs Introductory Code for the Heat Equation This repository provides some basic insights on Physics Informed Neural Networks (PINNs) and their implementation. […]

Published December 20, 2022

Federated Learning: Protect your data and privacy

Federated Learning: Protect your data and privacy Code: A basic PyTorch implementation of the FedAvg algorithm (GitHub) Federated Learning is becoming an […]

Published August 5, 2022

Gas networks at stationary states: Analysis, software and visualization

Gas networks at stationary states: Analysis, software and visualization Code: Files to run: nocircle.m, onecircle.m or twocircles.m   1 Introduction This post […]

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
Last news
  • FAU MoD Lecture: Bridging numerics and scientific machine learning for industrial applications
  • Benasque XI Workshop-Summer School 2026: Partial differential equations, optimal design and numerics
  • MLPDES26, Machine Learning and PDEs Workshop (2026)
  • FAU MoD Workshop G. Fantuzzi / D. Martonová
  • Development of a Modular Multi-Agent System Architecture for Enhanced Flexibility and Scalability

©  2019 - 2026  – 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