We study how spatiotemporal chaos in dynamical systems can be controlled by stochastically returning them to their initial conditions. Focusing on discrete nonlinear maps, we analyze how key measures of chaos—the Lyapunov exponent and butterfly velocity, which quantify sensitivity to initial perturbations and the ballistic spread of information, respectively—are reduced by stochastic resetting. We identify a critical resetting rate that induces a dynamical phase transition, characterized by the simultaneous vanishing of the Lyapunov exponent and butterfly velocity, effectively arresting the spread of information. These theoretical predictions are validated and illustrated with numerical simulations of the celebrated logistic map and its lattice extension. Beyond discrete maps, our findings are applicable to virtually any chaotic extended classical many-body system.

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• Chaos
• Control & applications of chaos
• Dynamical phase transitions
• Spatiotemporal chaos
• Stochastic resetting
• Chaotic systems
• Collective dynamics
• Dynamical systems
• Front propagation
• Lattice models in statistical physics
• Nonequilibrium lattice models
• Nonequilibrium systems
• Stochastic dynamical systems
• Traveling waves
• Chaos & nonlinear dynamics
• Markovian processes
• Nonequilibrium Green's function
• Statistical methods