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 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 […]

Published August 4, 2023

Combined convection and diffusion in a network. A numerical analysis

Combined convection and diffusion in a network. A numerical analysis. The problem: a contaminant in a network of water pipes Imagine that […]

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 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 […]

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
  • FAU MoD Lecture: Disruption in science and engineering happens at scale
  • #NdW25 Long Night Sciences: Sentimentanalyse mit Transformern in Aktion
  • #NdW25 Long Night Sciences: Das Turnpike-Phänomen in Gasnetzen
  • PhD Thesis defense by M. Hernández Salinas
  • Inverse design for parabolic and hyperbolic problems

©  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