[rkevingibson]

I'm a little late to this thread, but wanted to thank you for the paper! I found it interesting enough to blog about it (https://rkevingibson.github.io/blog/neural-networks-as-ordin...).

I wonder if you've given any thought to generalizing to fractional differential equations? My intuition tells me that the dynamics that you're learning are "local" in the sense that the ODE solvers depend only on the current state (and maybe some recent history), whereas learning the dynamics of a fractional system could give the system a larger "history" in the case of your time-series models.