  Accurate modeling of the Sun's coronal magnetic field and solar wind
structures require inputs of the solar global magnetic field, including both
the near and far sides, but the Sun's far-side magnetic field cannot be
directly observed. However, the Sun's far-side active regions are routinely
monitored by helioseismic imaging methods, which only require continuous
near-side observations. It is therefore both feasible and useful to estimate
the far-side magnetic-flux maps using the far-side helioseismic images despite
their relatively low spatial resolution and large uncertainties. In this work,
we train two machine-learning models to achieve this goal. The first
machine-learning training pairs simultaneous SDO/HMI-observed magnetic-flux
maps and SDO/AIA-observed EUV 304$\r{A}$ images, and the resulting model can
convert 304$\r{A}$ images into magnetic-flux maps. This model is then applied
on the STEREO/EUVI-observed far-side 304$\r{A}$ images, available for about 4.3
years, for the far-side magnetic-flux maps. These EUV-converted magnetic-flux
maps are then paired with simultaneous far-side helioseismic images for a
second machine-learning training, and the resulting model can convert far-side
helioseismic images into magnetic-flux maps. These helioseismically derived
far-side magnetic-flux maps, despite their limitations in spatial resolution
and accuracy, can be routinely available on a daily basis, providing useful
magnetic information on the Sun's far side using only the near-side
observations.
