Flash learning via localized perturbations of attractor dynamics

We present a control strategy for nonlinear dynamical systems that induces rapid state transitions through localized perturbations of attractor dynamics. This mechanism, which we term flash learning, exploits the sensitivity of basins of attraction to carefully timed impulses. Unlike continuous feedback methods, flash learning requires only transient, localized interventions yet produces enduring reorganization of accessible attractors. We demonstrate how this approach reshapes dynamical landscapes, enables efficient switching between stable states, and introduces an adaptive control paradigm within chaotic and multistable systems. Comparisons with classical control methods highlight flash learning as an efficient, low-energy alternative with potential applications across physical, biological, and computational domains.