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The operation of the electric power grid is foundational to the health, safety, and economic well-being of the nation, yet it is increasingly fragile and exposed to risk from exogenous factors. When power disruptions are widespread, prolonged, or impact critical services, the consequences can be grave. GMDs) can impact electric power transmission grids through premature ageing and transformer failure, which can lead to cascading failures and extended power disruptions. Geomagnetically induced currents (GICs) arising from geomagnetic disturbances (GMD mitigation poses a challenging problem to grid operators due to the nature of its impact. Space weather events arising from solar coronal mass ejections (CMEs) that intersect Earth's orbit occur on a continuum of timescales and levels of severity. Moderately sized CMEs, such as the 1989 event that lead to the failure of the HydroQuebec system, illustrate the risk to the power grid. Even larger space weather events that have the potential for profound impacts and prolonged power disruptions on a continental scale are thought to happen approximately storm has occurred during the existence of electric infrastructure. At the other end of the severity spectrum, recent evidence has shown that GICs flow at low levels continuously on the grid even in the every one hundred years; however, no such absence of a solar storm. This behavior may cause eventual, but slow to manifest breakdown of grid assets misattributed to non-GMD causes. In either case, it is difficult for utilities to justify the costly installation of sensors and telemetry to monitor this phenomenon. This paper details a system to augment human operators with two new abilities—(1) the real-time prediction of GICs flowing on their system and (2) the real-time monitoring of GMD grid manifestations—without installing new sensors. The system will do this by fusing a “top-down” approach using physics-based modeling driven by detailed 3-D Earth conductivity measurements and real-time magnetic observatory data with a “bottom-up” approach using artificial intelligence techniques driven by synchrophasor data. This hybrid methodology will enable utility operators to identify the best strategies to modify grid voltages and topology to mitigate damage and deal with a changing federal regulatory framework that requires GMD monitoring and mitigation efforts.