  Learning a continuous and reliable representation of physical fields from
sparse sampling is challenging and it affects diverse scientific disciplines.
In a recent work, we present a novel model called MMGN (Multiplicative and
Modulated Gabor Network) with implicit neural networks. In this work, we design
additional studies leveraging explainability methods to complement the previous
experiments and further enhance the understanding of latent representations
generated by the model. The adopted methods are general enough to be leveraged
for any latent space inspection. Preliminary results demonstrate the contextual
information incorporated in the latent representations and their impact on the
model performance. As a work in progress, we will continue to verify our
findings and develop novel explainability approaches.
