Speaker
Description
The evolution of the solar magnetic field is the key factor governing space weather drivers. Accurate forecasting of space weather requires precise modelling of the magnetic field's evolution on the solar surface using methods like Surface flux transport (SFT). Conventionally used SFT modelling techniques involve grid-based numerical schemes, making them computationally expensive. In this presentation, we present a novel, mesh-independent machine learning-based approach using Physics-Informed Neural Networks (PINNs) to simulate the temporal evolution of Bipolar Magnetic Regions (BMRs) on the solar photosphere. The ability of PINNs to solve advection-diffusion equations make it an efficient and accurate technique to simulate SFT equation. We employ this approach to study how nonlinear effects influence SFT models, with the broader goal of improving our understanding and constraints on solar dynamo processes. In particular, we focus on two mechanisms recently proposed to modulate solar cycle amplitudes: tilt quenching (TQ), representing a negative feedback between the cycle strength and the average tilt angle of active regions, and latitude quenching (LQ), indicating a positive relationship between cycle strength and the mean emergence latitude of active regions. Using PINNs within the SFT framework, we systematically examine the nonlinearities introduced by TQ, LQ, and their combined effects. Our study aims to clarify the distinct contributions of TQ and LQ to the solar dynamo. We find that the balance between LQ and TQ effects is closely linked to the ratio of meridional flow speed to magnetic diffusivity in the SFT models. Given that LQ is better constrained through observations, it may offer a valuable benchmark for refining solar dynamo models to achieve closer alignment with solar observations.
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