Description
The conversion of the toroidal field to the poloidal field in the solar dynamo is a nonlinear process influenced by mechanisms such as tilt quenching and latitude quenching according to the Babcock Lighton model. These nonlinearities play a crucial role in regulating the buildup of the Sun’s polar field. In this study, we investigate the effects of both quenching mechanisms using a Surface Flux Transport (SFT) model enhanced by Physics-Informed Neural Networks (PINNs). We compare our PINN-based simulations with traditional SFT models, both with and without nonlinearities, to assess the impact of data-driven approaches on modelling polar field evolution. Our results provide insights into the role of nonlinear quenching in solar cycle variability and offer a novel methodology for improving dynamo model predictions.
