/ Machine Learning

On Second Order Behaviour in Augmented Neural ODEs

On Second Order Behaviour in Augmented Neural ODEs

sonode

Official code for the paper On Second Order Behaviour in Augmented Neural ODEs (Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Liò)

quick_cos

nested_gif

Abstract

Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through
infinite-depth architectures. The continuous nature of NODEs has made them particularly suitable for learning the
dynamics of complex physical systems. While previous work has mostly been focused on first order ODEs, the dynamics of
many systems, especially in classical physics, are governed by second order laws. In this work, we take a closer look
at Second Order Neural ODEs (SONODEs). We show how the adjoint sensitivity method can be extended to SONODEs and prove
that an alternative first order optimisation method is computationally more efficient. Furthermore, we extend the
theoretical understanding of the broader class of Augmented NODEs (ANODEs) by showing they can also learn higher order
dynamics, but at the cost of interpretability. This indicates that the advantages of ANODEs go beyond the extra space
offered by the augmented dimensions, as originally thought. Finally, we compare SONODEs and ANODEs on synthetic and
real dynamical systems and demonstrate that the inductive biases of the former generally result in faster training
and better performance.

interpretability

ANODEs and SONODEs successfully learn the trajectory in real space of a 2D ODE for two different random initialisations.
However, the augmented trajectories of ANODE are in both cases widely different from the true velocity of the ODE.
In contrast, SONODE converges in both cases to the true ODE.

Getting started

We used python 3.7 for this project. To setup the virtual environment and necessary packages, please run the following commands:

$ conda create -n sonode python=3.7
$ conda activate sonode
$ pip install -r requirements.txt

You will also need to install PyTorch 1.4.0 from the official website.

Running the code

silverbox_combined_compressed

We provide a run.sh script for each experiment in the experiments folder.
All programs other than the MNIST experiments can be run on a cpu and typically finish within 2 hours
depending on the machine.

Citation

For attribution in academic contexts, please cite this work as

@article{norcliffe2020_sonode,
  title={On Second Order Behaviour in Augmented Neural ODEs},
  author={Alexander Norcliffe and Cristian Bodnar and Ben Day and Nikola Simidjievski and Pietro Li{\`o}},
  journal={arXiv preprint arXiv:2006.07220},
  year={2020}
}

GitHub

Comments