  Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite
operations and communications and affects the habitability of planets.
Currently, EUV-observing instruments are constrained to viewing the Sun from
its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for
other viewpoints (e.g. solar poles), and to generalize our knowledge of the
Sun-Earth system to other host stars. In this work, we adapt Neural Radiance
Fields (NeRFs) to the physical properties of the Sun and demonstrate that
non-ecliptic viewpoints could be reconstructed from observations limited to the
solar ecliptic. To validate our approach, we train on simulations of solar EUV
emission that provide a ground truth for all viewpoints. Our model accurately
reconstructs the simulated 3D structure of the Sun, achieving a peak
signal-to-noise ratio of 43.3 dB and a mean absolute relative error of 0.3\%
for non-ecliptic viewpoints. Our method provides a consistent 3D reconstruction
of the Sun from a limited number of viewpoints, thus highlighting the potential
to create a virtual instrument for satellite observations of the Sun. Its
extension to real observations will provide the missing link to compare the Sun
to other stars and to improve space-weather forecasting.
