Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

Yaofeng Desmond Zhong; Biswadip Dey; Amit Chakraborty

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arXiv · 2012.02334

Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

Yaofeng Desmond Zhong·Biswadip Dey·Amit Chakraborty

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Abstract

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.

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BibTeX RIS

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty. 2020-12-03. Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data. https://arxiv.org/abs/2012.02334

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