  Reservoir computers are powerful tools for chaotic time series prediction.
They can be trained to approximate phase space flows and can thus both predict
future values to a high accuracy, as well as reconstruct the general properties
of a chaotic attractor without requiring a model. In this work, we show that
the ability to learn the dynamics of a complex system can be extended to
systems with co-existing attractors, here a 4-dimensional extension of the
well-known Lorenz chaotic system. We demonstrate that a reservoir computer can
infer entirely unexplored parts of the phase space: a properly trained
reservoir computer can predict the existence of attractors that were never
approached during training and therefore are labelled as unseen. We provide
examples where attractor inference is achieved after training solely on a
single noisy trajectory.
