Classification and prediction of wave chaotic systems with machine learning techniques

Understand

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.