Turbulence-generating networks

The propagation of light through the atmosphere is often simulated with screens that impose a phase difference on an incoming wave. The screens are applied in series and have phase power spectral densities that are consistent with the strength of the turbulence to be simulated. We propose a new method to simulate the effect of turbulence on light propagation that uses mathematical graphs (networks) that are agnostic of spatial dimension or angle information. The intensity variation in the final screen is a measure of turbulence strength and we used several machine learning methods to adjust the probability distribution functions (PDF) of the weights of the edges of the networks to achieve the desired variations. We compare our results to the phase screen method for the case of the Kolmogorov spectrum using statistical measures such as the structure function. Finally, we explore avenues for improving the computational cost of producing turbulence-degraded images with this method as well as extending it to anisotropic turbulence.