Rodent: Relevance determination in differential equations
Abstract
We aim to identify the generating, ordinary differential equation (ODE) from a set of trajectories of a partially observed system. Our approach does not need prescribed basis functions to learn the ODE model, but only a rich set of Neural Arithmetic Units. For maximal explainability of the learnt model, we minimise the state size of the ODE as well as the number of non-zero parameters that are needed to solve the problem. This sparsification is realized through a combination of the Variational Auto-Encoder (VAE) and Automatic Relevance Determination (ARD). We show that it is possible to learn not only one specific model for a single process, but a manifold of models representing harmonic signals as well as a manifold of Lotka-Volterra systems.
Cite
@article{arxiv.1912.00656,
title = {Rodent: Relevance determination in differential equations},
author = {Niklas Heim and Václav Šmídl and Tomáš Pevný},
journal= {arXiv preprint arXiv:1912.00656},
year = {2020}
}