Speaker
Description
Understanding the solar photospheric magnetic field is vital for space weather forecasting. While Milne-Eddington (ME) inversion of spectropolarimetric data (Stokes I, Q, U, V for Fe I lines) is the standard for retrieving atmospheric parameters, it is computationally expensive, often taking hours per active region map on standard computers. To overcome this bottleneck, we apply a Physics-Informed Neural Network (PINN) to estimate the solar magnetic field from high-resolution observations. Our architecture embeds analytical ME radiative transfer equations into a Multilayer Perceptron (MLP) as physical constraints, ensuring predictions are physically grounded rather than strictly data-driven. Evaluations demonstrate that the PINN-generated synthetic Stokes profiles match observational data with high accuracy and noise resilience. This approach drastically reduces computational time, offering a robust and efficient alternative to classical inversions for rapid, large-scale solar data analysis.