arXiv 2002.088602020-02cs.LGcs.SYeess.SYstat.ML

Dissipative SymODEN：将耗散和控制的哈密顿动力学编码到深度学习中

Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

Princeton University（普林斯顿大学）；Siemens Corporation, Corporate Technology（西门子公司企业技术部）

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

备注：: Published at ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq)

摘要：

在这项工作中，我们介绍了耗散SymODEN，一个深度学习架构，它可以从观察到的状态轨迹中推断出具有耗散的物理系统的动力学。为了在减少网络规模的同时提高预测精度，耗散SymODEN将具有能量耗散和外部输入的端口-哈密顿动力学编码到其计算图的设计中，并以结构化的方式学习动力学。通过揭示系统的关键方面，如惯性，耗散和势能，学习模型为基于能量的控制器铺平了道路。

英文摘要：

In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To improve prediction accuracy while reducing network size, Dissipative SymODEN encodes the port-Hamiltonian dynamics with energy dissipation and external input into the design of its computation graph and learns the dynamics in a structured way. The learned model, by revealing key aspects of the system, such as the inertia, dissipation, and potential energy, paves the way for energy-based controllers.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。