  Here we present a laboratory analysis of the use of a 19-core photonic
lantern (PL) in combination with neural network (NN) algorithms as an efficient
focal plane wavefront sensor (FP-WFS) for adaptive optics (AO), measuring
wavefront errors such as low wind effect (LWE), Zernike modes and Kolmogorov
phase maps. The aberrated wavefronts were experimentally simulated using a
Spatial Light Modulator (SLM) with combinations of different phase maps in both
the linear regime (average incident RMS wavefront error (WFE) of 0.88 rad) and
in the non-linear regime (average incident RMS WFE of 1.5 rad). Results were
analysed using a NN to determine the transfer function of the relationship
between the incident wavefront error (WFE) at the input modes at the multimode
input of the PL and the intensity distribution output at the multicore fibre
outputs end of the PL. The root mean square error (RMSE) of the reconstruction
of petal and LWE modes were just $2.87\times10^{-2}$ rad and
$2.07\times10^{-1}$ rad respectively, in the non-linear regime. The
reconstruction RMSE for Zernike combinations ranged from $5.67\times10^{-2}$
rad to $8.43\times10^{-1}$ rad, depending on the number of Zernike terms and
incident RMS WFE employed. These results demonstrate the promising potential of
PLs as an innovative FP-WFS in conjunction with NNs.
