Implicit energy regularization of neural ordinary-differential-equation control

Abstract

Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. In this Letter we present a versatile neural ordinary-differential-equation control (NODEC) framework with implicit energy regularization and use it to obtain neural-network-generated control signals that can steer dynamical systems towards a desired target state within a predefined amount of time. We demonstrate the ability of NODEC to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that NODEC is capable to solve a wide range of control and optimization problems, including those that are analytically intractable.

5 pages, 3 figures

References in corpus (3)

• Phenomenological Models of Socio-Economic Network Dynamics
• Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions
• Learning Control of Quantum Systems

Cited by in corpus (13)

• Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
• Near-optimal control of dynamical systems with neural ordinary differential equations
• Surrogate Modeling and Control of Medical Digital Twins
• Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
• Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems
• Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control
• Continuous quantum gate sets and pulse class meta-optimization
• Visualizing high-dimensional loss landscapes with Hessian directions
• Control of dynamical systems with neural networks
• Physics-informed neural networks via stochastic Hamiltonian dynamics learning
• Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations
• Contractivity of the Method of Successive Approximations for Optimal Control
• Progressive Optimal Path Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks