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 12, 2023

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach Motivation This post shows the source code from the paper […]

Published December 9, 2021

Augmented Lagragian preconditioners for incompressible flow

Author: Alexei Gazca, FAU DCN-AvH Code:   Below is a description of the types of problems that can be tackled using the […]

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
  • Network design and control: Shape and topology optimization for the turnpike property for the wave equation
  • Reinforcement Learning and LQR with special control structure: switched and multilevel systems
  • FAU MoD Lecture: Data Driven Modeling for Scientific Discovery and Digital Twins
  • Exact Controllability of Stochastic First-Order Multi-Dimensional Hyperbolic Systems
  • Course: Introduction to Control and Machine Learning

©  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