  Coherent beam combination of multiple fibres can be used to overcome
limitations such as the power handling capability of single fibre
configurations. In such a scheme, the focal intensity profile is critically
dependent upon the relative phase of each fibre and so precise control over the
phase of each fibre channel is essential. Determining the required phase
compensations from the focal intensity profile alone (as measured via a camera)
is extremely challenging with a large number of fibres as the phase information
is obfuscated. Whilst iterative methods exist for phase retrieval, in practice,
due to phase noise within a fibre laser amplification system, a single step
process with computational time on the scale of milliseconds is needed. Here,
we show how a neural network can be used to identify the phases of each fibre
from the focal intensity profile, in a single step of ~ 10 milliseconds, for a
simulated 3-ring hexagonal close packed arrangement, containing 19 separate
fibres and subsequently how this enables bespoke beam shaping. In addition, we
show that deep learning can be used to determine whether a desired intensity
profile is physically possible within the simulation. This, coupled with the
demonstrated resilience against simulated experimental noise, indicates a
strong potential for the application of deep learning for coherent beam
combination.
