Learning Time Delay Systems with Neural Ordinary Differential Equations. (arXiv:2206.14288v2 [cs.LG] UPDATED)

A novel way of using neural networks to learn the dynamics of time delay
systems from sequential data is proposed. A neural network with trainable
delays is used to approximate the right hand side of a delay differential
equation. We relate the delay differential equation to an ordinary differential
equation by discretizing the time history and train the corresponding neural
ordinary differential equation (NODE) to learn the dynamics. An example on
learning the dynamics of the Mackey-Glass equation using data …

arxiv learning lg ordinary systems time