  The wave properties of complex scattering systems that are large compared to
the wavelength, and show chaos in the classical limit, are extremely sensitive
to system details. A solution to the wave equation for a specific configuration
can change substantially under small perturbations. Due to this extreme
sensitivity, it is difficult to discern basic information about a complex
system simply from scattering data as a function of energy or frequency, at
least by eye. In this work, we employ supervised machine learning algorithms to
reveal and classify hidden information about the complex scattering system
presented in the data. As an example we are able to distinguish the total
number of connected cavities in a linear chain of weakly coupled lossy
enclosures from measured reflection data. A predictive machine learning
algorithm for the future states of a perturbed complex scattering system is
also trained with a recurrent neural network. Given a finite training data
series, the reflection/transmission properties can be forecast by the proposed
algorithm.

Disordered Systems and Neural Networks;Chaotic Dynamics