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.
@article{arxiv.2012.02334,
title = {Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data},
author = {Yaofeng Desmond Zhong and Biswadip Dey and Amit Chakraborty},
journal= {arXiv preprint arXiv:2012.02334},
year = {2023}
}