Machine learning predictions for magnetic field time evolution in a Three-Meter liquid sodium spherical Couette experiment

The source of the Earth's magnetic field is the turbulent flow of liquid metal in the outer core. Our experiment's goal is to create an Earth-like dynamo to explore the mechanisms of generating the magnetic field and to understand the dynamics of the magnetic and velocity fields. We observe sub-dynamo states that show gain in the applied magnetic field. Prediction of the magnetic field in MHD turbulence field is a challenging problem. We present results of mimicking the experiment by a reservoir computer deep learning algorithm and compare the results with predictions that are done by other techniques. The experiment is a three-meter diameter outer sphere and a one-meter diameter inner core model with the gap filled with liquid sodium. The spheres can rotate independently up to 4 and 14 Hz respectively, giving a Reynolds number up to 1.5*10⁸. Two external electromagnets apply magnetic fields, while 33 Hall sensors measure the resulting fields. We use this data to train a reservoir computer to predict the time evolution and mimic waves in the experiment. Surprisingly accurate predictions can be made for several magnetic dipole time scales. This shows that such a complicated MHD system’s behavior can be predicted.