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

Robust neural ODEs

Published June 20, 2022

The code implements the gradient regularization method of robust training in the setting of neural ODEs.
Various jupyter notebooks are included that generate plots comparing standard to robust training for 2d point clouds.

Code:

A good starting point is robustness_plots.ipynb

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

 

|| Go to the Math & Research main page

You may also like

Published April 7, 2022

The interplay of control and deep learning

Author: Borjan Geshkovski, MIT The interplay of control and Deep Learning By Borjan Geshkovski   It is superfluous to state the impact […]

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 […]

Published September 13, 2022

Training of neural ODEs using pyTorch

Start with tutorials to get familiar with the code Tutorial 1: Train a neural ODE based network on point cloud data set […]

Post navigation

  • Previous post Optimal design of sensors and actuators by E. Zuazua
  • Back to post list
  • Next post Mini-workshop: “Recent Advances in Analysis and Control”
Last news
  • Enrique Zuazua invited to deliver a Special Section Lecture at ICM 2026
  • FAU MoD Lecture: Hybrid Modeling and System Identification: Past and Future Directions
  • EZ 65: Control, PDEs and Machine Learning
  • In search of the origins of matrix multiplication
  • Trends in Mathematical Sciences II (2026)

©  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