  Picosecond laser pulses have been used as a surface colouring technique for
noble metals, where the colours result from plasmonic resonances in the
metallic nanoparticles created and redeposited on the surface by ablation and
deposition processes. This technology provides two datasets which we use to
train artificial neural networks, data from the experiment itself (laser
parameters vs. colours) and data from the corresponding numerical simulations
(geometric parameters vs. colours). We apply deep learning to predict the
colour in both cases. We also propose a method for the solution of the inverse
problem -- wherein the geometric parameters and the laser parameters are
predicted from colour -- using an iterative multivariable inverse design
method.
