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Neural Ordinary Differential Equation Control of Dynamics on Graphs
[article]
2021
arXiv
pre-print
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
arXiv:2006.09773v5
fatcat:6oiktynaqbdz5bqzcetrgn5k4y