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 July 3, 2023

Reinforcement learning as a new perspective into controlling physical systems

Reinforcement learning as a new perspective into controlling physical systems Introduction Optimal control addresses the problem of bringing a system from an […]

Published July 23, 2022

Lloyd’s Algorithm

Author: Martín Hernández, FAU DCN-AvH Code: In this repository, we show a code for Lloyd’s algorithm. Also called Voronoid iteration, this is […]

Published July 3, 2024

Clustering in pure-attention hardmax transformers and its role in sentiment analysis

Clustering in pure-attention hardmax transformers and its role in sentiment analysis This post provides an overview of the results in the paper […]

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 […]

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
  • PhD Thesis defense by A. Alvarez-Lopez
  • MLPDES26, Machine Learning and PDEs Workshop (2026)
  • FAU MoD Lecture: Reverse typography and the theory of shape: Can old books be brought back to life?
  • Sums-of-squares Polyconvexity
  • Enrique Zuazua invited to deliver a Special Section Lecture at ICM 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