Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. (arXiv:2205.11894v1 [cs.LG])

We study for the first time uncertainty-aware modeling of continuous-time
dynamics of interacting objects. We introduce a new model that decomposes
independent dynamics of single objects accurately from their interactions. By
employing latent Gaussian process ordinary differential equations, our model
infers both independent dynamics and their interactions with reliable
uncertainty estimates. In our formulation, each object is represented as a
graph node and interactions are modeled by accumulating the messages coming
from neighboring objects. We show that efficient inference of …

arxiv learning process systems