研究非线性淬灭效应如何影响太阳极场增长
Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks
AI总结：
本文利用物理信息神经网络研究太阳极场增长中的非线性淬灭效应，通过改变输运参数分离TQ和LQ的贡献，发现TQ随扩散率增强而抑制，LQ主导对流主导区域，改进了太阳动力学模型的准确性。
摘要：
太阳动力学依赖于通过非线性反馈机制如倾斜淬灭（TQ）和纬度淬灭（LQ）再生极磁场。这些机制在调节太阳极场增长及未来太阳活动周期振幅中起决定性作用。本文利用物理信息神经网络（PINN）求解表面流输运（SFT）方程，将物理约束直接嵌入神经网络框架。通过系统地改变输运参数，我们分离了TQ和LQ对极二极子增长的相对贡献。我们使用残余二极子矩作为诊断指标，显示TQ抑制随扩散率增加而增强，而LQ主导对流主导区域。比值ΔD_LQ/ΔD_TQ表现出与动力学有效性范围的平方反比关系，改进了之前的经验拟合，提高了准确性和减少了散射。结果进一步表明，PINN设置中衰减项的必要性并非由于训练过程。与传统1D SFT模型相比，PINN框架实现了显著降低的误差指标和更稳健的非线性趋势恢复。我们的结果表明，LQ和TQ之间的非线性相互作用可以自然产生弱强周期交替，为观察到的偶奇周期调制提供了物理解释。这些发现展示了PINN作为准确、高效且物理一致的太阳周期预测工具的潜力。
英文摘要：
The solar dynamo relies on the regeneration of the poloidal magnetic field through processes strongly modulated by nonlinear feedbacks such as tilt quenching (TQ) and latitude quenching (LQ). These mechanisms play a decisive role in regulating the buildup of the Sun's polar field and, in turn, the amplitude of future solar cycles. In this work, we employ Physics-Informed Neural Networks (PINN) to solve the surface flux transport (SFT) equation, embedding physical constraints directly into the neural network framework. By systematically varying transport parameters, we isolate the relative contributions of TQ and LQ to polar dipole buildup. We use the residual dipole moment as a diagnostic for cycle-to-cycle amplification and show that TQ suppression strengthens with increasing diffusivity, while LQ dominates in advection-dominated regimes. The ratio $ΔD_{\mathrm{LQ}}/ΔD_{\mathrm{TQ}}$ exhibits a smooth inverse-square dependence on the dynamo effectivity range, refining previous empirical fits with improved accuracy and reduced scatter. The results further reveal that the need for a decay term is not essential for PINN set-up due to the training process. Compared with the traditional 1D SFT model, the PINN framework achieves significantly lower error metrics and more robust recovery of nonlinear trends. Our results suggest that the nonlinear interplay between LQ and TQ can naturally produce alternations between weak and strong cycles, providing a physical explanation for the observed even-odd cycle modulation. These findings demonstrate the potential of PINN as an accurate, efficient, and physically consistent tool for solar cycle prediction.
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