This repository contains the source code and additional resources for the paper "Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models". The paper discusses the challenges of solar wind forecasting and the application of Physics-Informed Neural Networks (PiNNs) to improve prediction accuracy and computational efficiency.
Space weather refers to the dynamic conditions in the solar system, particularly the interactions between the solar wind - a stream of charged particles emitted by the Sun - and the Earth's magnetic field and atmosphere. Accurate space weather forecasting is crucial for mitigating potential impacts on satellite operations, communication systems, power grids, and astronaut safety. However, existing models like MULTI-VP require substantial computational resources. This paper proposes a Physics-Informed Neural Network (PiNN) as a faster yet accurate alternative that respects physical laws. PiNNs blend physics and data-driven techniques for rapid and reliable forecasts. Our studies show that PiNNs can reduce computation times and deliver forecasts comparable to MULTI-VP, offering an expedited and dependable solar wind forecasting approach.
The model uses data derived from magnetogram observations and solar wind parameters processed through the MULTI-VP model. Note that due to data privacy and licensing issues, the dataset used in the study is not publicly available in this repository. For access to the data or further inquiries, contact the authors directly.
- Nuno Costa
- Filipa S. Barros
- J.J.G. Lima
- Rui F. Pinto
- André Restivo
This project is licensed under the MIT License - see the LICENSE.md file for details.