  Catastrophe theory was originally proposed to study dynamical systems that
exhibit sudden shifts in behavior arising from small changes in input. These
models can generate reasonable explanation behind abrupt jumps in nonlinear
dynamic models. Among the different catastrophe models, the Cusp Catastrophe
model attracted the most attention due to it's relatively simpler dynamics and
rich domain of application. Due to the complex behavior of the response, the
parameter space becomes highly non-convex and hence it becomes very hard to
optimize to figure out the generating parameters. Instead of solving for these
generating parameters, we demonstrated how a Machine learning model can be
trained to learn the dynamics of the Cusp catastrophe models, without ever
really solving for the generating model parameters. Simulation studies and
application on a few famous datasets are used to validate our approach. To our
knowledge, this is the first paper of such kind where a neural network based
approach has been applied in Cusp Catastrophe model.
