Physics Informed Deep Learning for Real Time Prediction of Solar Terrestrial Plasma and Global Climate Change

Abstract

The ever-growing dynamism of solar-terrestrial plasma is a serious risk to the stability of satellite communication in the world, the stability of power grids, and the stability of the climate in the long term. The classical numerical models are physically consistent and tend to be infeasible in real-time applications. On the other hand, standard models of deep learning are quickly inferred and are physically inconsistent in regimes of exploration. The paper suggests a Physics-Informed Deep Learning (PIDL) architecture for the high-fidelity and real-time forecasting of solar-terrestrial plasma dynamics and its secondary effects on global climate change. The algorithm will include the underlying equations of magnetohydrodynamics (MHD) and the radiative transfer constraints directly in the neural network's loss function, ensuring the model complies with the laws of conservation of mass and momentum. The framework fills the gap between space weather nowcasting, on the one hand, and predictive econometric analysis of climate transitions, on the other hand, on the basis of a multi-source dataset, made of GPS-based Total Electron Content (TEC) data, magnetospheric plasma density data, and solar irradiance index data. Through statistical analysis, it was established that the Physics-Informed Deep Learning methodology is much superior to traditional architectures with a Root Mean Square Error (RMSE) of 0.245, which is 21.5% lower than the standard CNN-LSTM architecture. Moreover, the framework shows a higher physical validity and convergence within the extreme solar events, whereby traditional black-box models are usually divergent. The work concludes that to have the accuracy needed to model particle precipitation at the next generation and to predict the long-term effects of the environment, it is necessary to integrate physical priors into deep learning architectures. The study provides a scalable alternative to the stakeholders in the aerospace, telecommunication, and environmental policy to reduce the risks of the swift atmospheric transformations in response to solar.