Document Type
Article
Author ORCID Identifier
Soukaina Filali Boubrahimi https://orcid.org/0000-0001-5693-6383
Journal/Book Title/Conference
Universe
Volume
10
Issue
5
Publisher
MDPI AG
Publication Date
4-24-2024
Journal Article Version
Version of Record
First Page
1
Last Page
20
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model's performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models.
Recommended Citation
Johnson, R.; Filali Boubrahimi, S.; Bahri, O.; Hamdi, S.M. Combining Empirical and Physics-Based Models for Solar Wind Prediction. Universe 2024, 10, 191. https://doi.org/10.3390/universe10050191
