edu.ucar.opensky::articles:43715 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:49:23.059616 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d7db869v https://doi.org/10.18434/T4W30F https Related Dataset #1 NIST Atomic Spectra Database, NIST Standard Reference Database 78 https://doi.org/10.5281/zenodo.15189930 https Related Software #1 PINN ME v0.1.0 PINN ME: A physics-informed neural network framework for accurate Milne–Eddington inversions of solar magnetic fields 2025-05-20T00:00:00Z publication https://n2t.net/ark:/85065/d7db869v https://doi.org/10.3847/2041-8213/add342 Jarolim, Robert author Molnar, Momchil author Tremblay, Benoit author Centeno, Rebecca author Rempel, Matthias author UCAR/NCAR - Library publisher Spectropolarimetric inversions of solar observations are fundamental for the estimation of the magnetic field in the solar atmosphere. 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&#8211;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&#8211;Eddington parameters that best fit the observations. In addition, we directly include the point-spread function to account for instrumental effects. We use a predefined parameter space as well as synthetic profiles from a radiative MHD simulation to evaluate the performance of our method and to estimate the impact of instrumental noise. Our results demonstrate that PINN ME achieves an intrinsic spatiotemporal coupling, which can largely mitigate observational noise and provides a memory-efficient inversion even for extended fields of view. Finally, we apply our method to observations and show that our method provides a high spatial coherence and can resolve small-scale features in both strong- and weak-field regions. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation