Speaker
Description
We present a data-driven and artificial intelligence-based method for the three-dimensional characterization of magnetic field lines in the solar atmosphere, applied to NOAA AR 3663, one of the most complex and eruptive active regions of Solar Cycle 25. The method is based on the extrapolation of the nonlinear force-free coronal magnetic field (NLFFF) via Physically Informed Neural Networks (PINNs), using vector magnetograms from the SDO/HMI as a boundary condition, following the formulation of Jarolim et al. (2024). From the 3D volume generated by the PINN, the field lines are reconstructed by bidirectional numerical integration with the 4th-order Runge-Kutta algorithm (RK4). The morphological validation of the magnetic structures is performed by comparing them with coronal loops observed in extreme ultraviolet (EUV) images from the SDO/AIA. To complement the magnetic modeling ($\beta \approx 0$) with independent thermodynamic constraints, we performed Differential Emission Measurement (DEM) analysis using the CHIANTI atomic database, determining the electron density and temperature distribution along the reconstructed loops. Preliminary results reveal the geometric distribution (length and height) of the loops and the thermal structure of AR 3663, providing insights into free energy storage and the magnetic conditions that precede eruptive activity. The method demonstrates the potential of PINN-based NLFFF solvers as physically consistent and computationally efficient tools for coronal diagnostics and modeling of space weather originating in active regions.