Neural Networks for Surrogate Models of the Corona and Solar Wind

In previous work, an Artificial Neural Network (ANN) was developed to automate the estimation of solar wind profiles used as initial conditions in MULTI-VP simulations. This approach, coupled with profile clustering, reduced the time previously required for estimation by MULTI-VP, enhancing the efficiency of the simulation process. It was observed that generating initial estimates closer to the final simulation led to reduced computation time, with a mean speedup of 1.13. Additionally, this adjustment yielded a twofold advantage: it minimized the amplitude of spurious transients, reinforcing the numerical stability of calculations and enabling the code to maintain a more moderate integration time step.

However, upon further analysis, it became evident that the physical model inherently required a relaxation time for the final solution to stabilize. Therefore, while refining initial conditions offered improvements, there was a limit to how much it could accelerate the process. Consequently, attention turned towards the development of a surrogate model focused on the upper corona (from 3 solar radii to 30 solar radii). This range was chosen because the model can avoid learning the initial phases of wind acceleration, which are hard to accurately predict. Moreover, in order to connect the model to heliospheric models and for space weather applications, more than 3 radii is more than sufficient and guarantees that the physics remain consistent within the reproducible domain.

This surrogate model aims at delivering faster forecasts, with MULTI-VP running in parallel (eventually refining the solutions). The surrogate model for MULTI-VP was tested using a heliospheric model and data from spacecraft at L1, validating its efficacy beyond Mean Squared Error (MSE) evaluations and ensuring physical conservation principles were upheld.

This work aims at simplifying and accelerating the process of establishing boundary conditions for heliospheric models without dismissing the physical models for both extreme events and for more physically accurate results.