  Recent advances in learning techniques have enabled the modelling of
dynamical systems for scientific and engineering applications directly from
data. However, in many contexts explicit data collection is expensive and
learning algorithms must be data-efficient to be feasible. This suggests using
additional qualitative information about the system, which is often available
from prior experiments or domain knowledge. We propose an approach to learning
a vector field of differential equations using sparse Gaussian Processes that
allows us to combine data and additional structural information, like Lie Group
symmetries and fixed points. We show that this combination improves
extrapolation performance and long-term behaviour significantly, while also
reducing the computational cost.
