  We introduce a method for inferring an explicit PDE from a data sample
generated by previously unseen dynamics, based on a learned context. The
training phase integrates knowledge of the form of the equation with a
differential scheme, while the inference phase yields a PDE that fits the data
sample and enables both signal prediction and data explanation. We include
results of extensive experimentation, comparing our method to SOTA approaches,
together with ablation studies that examine different flavors of our solution.
