PINN ME: A physics-informed neural network framework for accurate Milne–Eddington inversions of solar magnetic fields

However, instrumental noise, computational requirements, and varying levels of physical realism make it challenging to derive reliable solar magnetic field estimates. In this study, we present a novel approach for spectropolarimetric inversions based on physics-informed neural networks to infer the photospheric magnetic field under the Milne–Eddington approximation (PINN ME).

Our model acts as a representation of the parameter space, mapping input coordinates ( t , x , y ) to the respective spectropolarimetric parameters, which are used to synthesize the corresponding Stokes profiles. By iteratively sampling coordinate points, synthesizing profiles, and minimizing the deviation from the observed stokes profiles, our method can find the set of Milne–Eddington parameters that best fit the observations.