Steering the dynamics by controlling the temporal interaction network
Abstract
Many real-world coupled dynamical systems have their interaction structure and strength that evolve or adapt over time. Here, we investigate how one can control the state of a system by tuning its temporal interaction network. We present a framework based on nonlinear optimal control, where one has control over the time-dependent coupling matrix of a dynamical system. We show how to obtain the gradient of the Lagrangian functional of a generic optimal control problem using the adjoint method, where the control variables are multiplicative inputs. We illustrate the theory with the precision cost that aims at steering the nodal states to prescribed target trajectories. We first focus on a linear time-variant system for which further expressions of the gradient can be obtained. Second, we uncover the temporal interaction that pushes a nonlinear neural units model towards target trajectories. For both systems, we numerically solve the optimal control problem, imposing various constraint on the structure of the controlled edges. The workflow presented here can be leveraged to steer the dynamics of systems with artificial or engineered interaction that is tunable.
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BibTeX
@misc{tyloo2026steering,
title = {{Steering the dynamics by controlling the temporal interaction network}},
author = {Melvyn Tyloo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.04998},
eprint = {2607.04998},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.04998},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Steering the dynamics by controlling the temporal interaction network
AU - Melvyn Tyloo
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2607.04998
UR - https://arxiv.org/abs/2607.04998
AB - Many real-world coupled dynamical systems have their interaction structure and strength that evolve or adapt over time. Here, we investigate how one can control the state of a system by tuning its temporal interaction network. We present a framework based on nonlinear optimal control, where one has control over the time-dependent coupling matrix of a dynamical system. We show how to obtain the gradient of the Lagrangian functional of a generic optimal control problem using the adjoint method, where the control variables are multiplicative inputs. We illustrate the theory with the precision cost that aims at steering the nodal states to prescribed target trajectories. We first focus on a linear time-variant system for which further expressions of the gradient can be obtained. Second, we uncover the temporal interaction that pushes a nonlinear neural units model towards target trajectories. For both systems, we numerically solve the optimal control problem, imposing various constraint on the structure of the controlled edges. The workflow presented here can be leveraged to steer the dynamics of systems with artificial or engineered interaction that is tunable.
ER -