Near-optimal control of dynamical systems with neural ordinary differential equations. (arXiv:2206.11120v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11120

Optimal control problems naturally arise in many scientific applications
where one wishes to steer a dynamical system from a certain initial state
$\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time $T$.
Recent advances in deep learning and neural network-based optimization have
contributed to the development of methods that can help solve control problems
involving high-dimensional dynamical systems. In particular, the framework of
neural ordinary differential equations (neural ODEs) provides an efficient
means to iteratively approximate continuous time control …

arxiv lg near neural ordinary systems