Thanks! If I understand your question, you're asking if the ODE time-series model we proposed works best in things like idealized frictionless or gravity-less

Thanks! If I understand your question, you're asking if the ODE time-series model we proposed works best in things like idealized frictionless or gravity-less setting? The answer is that we expect it to work (in principle) even for messy, non-physics-experiment-style situations. This because we don't model the system dynamics directly, but in a latent space that we learn. So even in a messy situation like health monitoring of humans, as long as the data are being driven mostly by hidden factors that are changing smoothly through time (like overall health, infections, hormones or whatever) the neural ODE can abstract away from the messy data. This is all still just in principle - there are a few technical challenges to scaling these models up. But nothing that looks insurmountable right now.