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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., Comment: Accepted at Machine Learning and the Physical Sciences workshop, NeurIPS 2022

The input of the Solar wind models plays a significant role in accurate solar wind predictions at 1 AU. This work introduces a synthetic magnetogram produced from a dynamo model as an input for Magnetohydrodynamics (MHD) simulations. We perform a quantitative study that compares the Space Weather Modeling Framework (SWMF) results for the observed and the synthetic solar magnetogram input. For each case, we compare the results for Extreme Ultra-Violet (EUV) images and extract the simulation data along the earth trajectory to compare with in-situ observations. We initialize SWMF using the real and synthetic magnetogram for a set of Carrington Rotations (CR)s within the solar cycle 23 and 24. Our results help quantify the ability of dynamo models to be used as input to solar wind models and thus, provide predictions for the solar wind at 1 AU.