  We present a novel procedure for manipulating the near-field of plasmonic
nanoantennas using neural network-controlled laser pulse-shaping. As model
systems we numerically studied the spatial distribution of the second harmonic
response of L-shaped nanoantennas illuminated by broadband laser pulses. We
first show that a trained neural network can be used to predict the relative
intensity of the second-harmonic hotspots of the nanoantenna for a given
spectral phase and that it can be employed to deterministically switch
individual hotspots on and off on sub-diffraction length scale by shaping the
spectral phase of the laser pulse. We then demonstrate that a neural network
trained on a $90~\mathrm{nm} \times 150~\mathrm{nm}$ nano-L can in addition
efficiently predict the hotspot intensities in an antenna with different aspect
ratio after minimal further training for varying spectral phases. These results
could lead to novel applications of machine-learning and optical control to
nanoantennas and nanophotonics components.
