  Neural ODE Processes approach the problem of meta-learning for dynamics using
a latent variable model, which permits a flexible aggregation of contextual
information. This flexibility is inherited from the Neural Process framework
and allows the model to aggregate sets of context observations of arbitrary
size into a fixed-length representation. In the physical sciences, we often
have access to structured knowledge in addition to raw observations of a
system, such as the value of a conserved quantity or a description of an
understood component. Taking advantage of the aggregation flexibility, we
extend the Neural ODE Process model to use additional information within the
Learning Using Privileged Information setting, and we validate our extension
with experiments showing improved accuracy and calibration on simulated
dynamics tasks.
