论文标题
耗散共生式：用耗散编码汉密尔顿动态并控制深度学习
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
论文作者
论文摘要
在这项工作中，我们介绍了耗散共性植物，这是一种深度学习的结构，可以从观察到的状态轨迹中推断出物理系统的动力学。为了提高预测准确性，同时降低网络大小，耗散共子词以能量耗散和外部输入编码港口 - 哈米尔顿港动力学，并以结构化的方式学习动力学。通过揭示系统的关键方面（例如惯性，耗散和势能），该模型为基于能量的控制器铺平了道路。
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.
