论文标题
基准基准能量支持的神经网络从数据中学习动态
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
论文作者
论文摘要
最近几年，人们对将物理知识的归纳偏见纳入深度学习框架中引起了人们的兴趣。特别是，越来越多的文献正在探索如何从观察到的时间序列数据中学习动态的同时，在使用神经网络来执行节能的方法。在这项工作中，我们调查了十项最近提出的持势持续能量的神经网络模型，包括HNN，LNN，DELAN，SIMODEN，CHNN，CLNN及其变体。我们为这些模型背后的理论提供了紧凑的推导，并解释了它们的相似性和差异。在4个物理系统中比较它们的性能。我们指出了利用其中一些能源模型来设计基于能量的控制器的可能性。
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.
