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 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 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 […]

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 September 30, 2022

Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs)

Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs)   Introduction Accurate and fast predictions of numerical solutions are of […]

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
  • MLDS: Machine Learning, Control Theory, and PDEs: Foundations and Advances from Variational Pathologies to Diffusion Models for Generative AI
  • FAU MoD Lecture: AI Components in PDE Solvers
  • #NdW25 Long Night Sciences: Sentimentanalyse mit Transformern in Aktion
  • #NdW25 Long Night Sciences: Das Turnpike-Phänomen in Gasnetzen
  • FAU MoD Lecture: Finding the optimal model complexity of whole-brain models and digital twins

©  2019 - 2025  – 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