  This paper proposes a semi-supervised methodology for training
physics-informed machine learning methods. This includes self-training of
physics-informed neural networks and physics-informed Gaussian processes in
isolation, and the integration of the two via co-training. We demonstrate via
extensive numerical experiments how these methods can ameliorate the issue of
propagating information forward in time, which is a common failure mode of
physics-informed machine learning.
