Physics-informed time-dependent deep neural network for solar wind prediction

In this work, we aim to advance the prediction of solar wind speed several days in advance. The approach is based on analyzing solar coronal images in conjunction with solar wind speed.  We create labelled data pairs from over a decade of EUV images obtained from the SDO/AIA and solar wind data at 1AU recorded by ACE, WIND, and DISCOVR.  We use the archived SDO machine-learning ready dataset (SDO-ML), and the solar wind speed at 1AU from the NASA OMNIWEB dataset. We construct a deep neural network model and capture the temporal component of the solar wind propagation with a time-dependent neural network, e.g., Recurrent Neural Network. Physical constraints are incorporated to train the model and optimize the prediction. The generalization capability of our model is investigated via cross-validation, whereby careful separation into training, validation, and test datasets is performed as a function of solar activity. We report on the impact of the deep neural network architecture as a universal function approximation in its ability to capture the temporal relationship between solar EUV characteristics and solar wind speed at 1 AU.