  The last few years have witnessed an increased interest in incorporating
physics-informed inductive bias in deep learning frameworks. In particular, a
growing volume of literature has been exploring ways to enforce energy
conservation while using neural networks for learning dynamics from observed
time-series data. In this work, we survey ten recently proposed
energy-conserving neural network models, including HNN, LNN, DeLaN, SymODEN,
CHNN, CLNN and their variants. We provide a compact derivation of the theory
behind these models and explain their similarities and differences. Their
performance are compared in 4 physical systems. We point out the possibility of
leveraging some of these energy-conserving models to design energy-based
controllers.
