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Steering the dynamics by controlling the temporal interaction network
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
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