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Model-free and data-driven prediction of tipping point transitions innonlinear dynamical systems is a challenging and outstanding task in complexsystems science. We propose a novel, fully data-driven machine learningalgorithm based on next-generation reservoir computing to extrapolate thebifurcation behavior of nonlinear dynamical systems using stationary trainingdata samples. We show that this method can extrapolate tipping pointtransitions. Furthermore, it is demonstrated that the trained next-generationreservoir computing architecture can be used to predict non-stationary dynamicswith time-varying bifurcation parameters. In doing so, post-tipping pointdynamics of unseen parameter regions can be simulated.

Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning