  In this paper, we propose a framework for performing state space exploration
of closed loop control systems. Our approach involves approximating sensitivity
and a newly introduced notion of inverse sensitivity by a neural network. We
show how the approximation of sensitivity and inverse sensitivity can be used
for computing estimates of the reachable set. We then outline algorithms for
performing state space exploration by generating trajectories that reach a
neighborhood. We demonstrate the effectiveness of our approach by applying it
not only to standard linear and nonlinear dynamical systems, but also to
nonlinear hybrid systems and also neural network based feedback control
systems.
