  Manipulation of light-induced magnetization has become a fundamentally hot
topic with a potentially high impact for atom trapping, confocal and magnetic
resonance microscopy, and data storage. However, the control of the
magnetization orientation mainly relies on the direct methods composed of
amplitude, phase and polarization modulations of the incident light under the
tight focusing condition, leaving arbitrary three-dimensional (3D)
magnetization orientation completely inaccessible. In this letter, we propose a
facile approach called machine learning inverse design to achieve arbitrary and
expected vectorial magnetic holography. This method is time-efficient and
accurate to produce the demanded incident beam for arbitrary prescribed 3D
magnetization orientation. We believe that the machine learning method is not
only applied for magnetization orientations, but also widely used in the
control of magnetization structures.
