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This paper presents a novel approach to simulating coupled dynamical systems by using neural surrogates and self-consistency to solve fixed-point problems over full trajectories, replacing traditional timestep-by-timestep integration. It demonstrates the method on van der Pol oscillators and Hodgkin–Huxley neuron networks, highlighting efficiency gains and unique gradient properties.