  In the field of complex dynamics, multistable attractors have been gaining a
significant attention due to its unpredictability in occurrence and extreme
sensitivity to initial conditions. Co-existing attractors are abundant in
diverse systems ranging from climate to finance, ecological to social systems.
In this article, we investigate a data-driven approach to infer different
dynamics of a multistable system using echo state network (ESN). We start with
a parameter-aware reservoir and predict diverse dynamics for different
parameter values. Interestingly, machine is able to reproduce the dynamics
almost perfectly even at distant parameters which lie considerably far from the
parameter values related to the training dynamics. In continuation, we can
predict whole bifurcation diagram significant accuracy as well. We extend this
study for exploring various dynamics of multistable attractors at unknown
parameter value. While, we train the machine with the dynamics of only one
attarctor at parameter $p$, it can capture the dynamics of co-existing
attractor at a new parameter value $p+\Delta p$. Continuing the simulation for
multiple set of initial conditions, we can identify the basins for different
attractors. We generalize the results by applying the scheme on two distinct
multistable systems.
