  There is significant interest in using modern neural networks for scientific
applications due to their effectiveness in modeling highly complex, non-linear
problems in a data-driven fashion. However, a common challenge is to verify the
scientific plausibility or validity of outputs predicted by a neural network.
This work advocates the use of known scientific constraints as a lens into
evaluating, exploring, and understanding such predictions for the problem of
inertial confinement fusion.
