论文标题
神经通用的微分方程对图上的动力学控制
Neural Ordinary Differential Equation Control of Dynamics on Graphs
论文作者
论文摘要
我们研究了神经网络计算反馈控制信号的能力，即在图上引导连续时间非线性动力学系统的轨迹，我们用神经常规微分方程（神经ODE）表示。为此，我们提出了一个神经码控制（NODEC）框架，发现它可以学习将图形动态系统驱动到所需目标状态的反馈控制信号。尽管我们使用不限制控制能量的损失函数，但我们的结果表明，根据相关工作，NODEC会产生低能控制信号。最后，我们评估了NODEC对众所周知的反馈控制者和深入的强化学习的性能和多功能性。我们使用NODEC来生成一千多个耦合的非线性ODES系统的反馈控件，该系统代表流行过程和耦合振荡器。
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we present a neural-ODE control (NODEC) framework and find that it can learn feedback control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show, in accordance with related work, that NODEC produces low energy control signals. Finally, we evaluate the performance and versatility of NODEC against well-known feedback controllers and deep reinforcement learning. We use NODEC to generate feedback controls for systems of more than one thousand coupled, non-linear ODEs that represent epidemic processes and coupled oscillators.
