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Solar magnetic fields play a key role in understanding the nature of thecoronal phenomena. Global coronal magnetic fields are usually extrapolated fromphotospheric fields, for which farside data is taken when it was at thefrontside, about two weeks earlier. For the first time we have constructed theextrapolations of global magnetic fields using frontside and artificialintelligence (AI)-generated farside magnetic fields at a near-real time basis.We generate the farside magnetograms from three channel farside observations ofSolar Terrestrial Relations Observatory (STEREO) Ahead (A) and Behind (B) byour deep learning model trained with frontside Solar Dynamics Observatoryextreme ultraviolet images and magnetograms. For frontside testing data sets,we demonstrate that the generated magnetic field distributions are consistentwith the real ones; not only active regions (ARs), but also quiet regions ofthe Sun. We make global magnetic field synchronic maps in which conventionalfarside data are replaced by farside ones generated by our model. Thesynchronic maps show much better not only the appearance of ARs but also thedisappearance of others on the solar surface than before. We use thesesynchronized magnetic data to extrapolate the global coronal fields usingPotential Field Source Surface (PFSS) model. We show that our results are muchmore consistent with coronal observations than those of the conventional methodin view of solar active regions and coronal holes. We present several positiveprospects of our new methodology for the study of solar corona, heliosphere,and space weather.

Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside