Graph-based Neural Space Weather Forecasting

2025-09-23

AI Summary

Current space weather forecasting relies on computationally expensive physics-based models (e.g., Vlasiator), which are infeasible for real-time operation and lack inherent uncertainty quantification. To address this, we propose a graph neural network (GNN)-based surrogate model that performs autoregressive forecasting of near-Earth space environment time series, driven solely by solar wind parameters. We further introduce a generative ensemble modeling framework—marking the first systematic uncertainty quantification in data-driven space weather prediction. Trained on high-fidelity Vlasiator simulation data, our approach ensures physical consistency while achieving exceptional computational efficiency: deterministic forecasts match the accuracy of the original physics model, with inference speed accelerated by two to three orders of magnitude. The calibrated uncertainty ensembles enable rigorous risk assessment and informed decision support. This work establishes a new paradigm for operational, trustworthy space weather forecasting.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web search models and ranking

Abstract

Accurate space weather forecasting is crucial for protecting our increasingly digital infrastructure. Hybrid-Vlasov models, like Vlasiator, offer physical realism beyond that of current operational systems, but are too computationally expensive for real-time use. We introduce a graph-based neural emulator trained on Vlasiator data to autoregressively predict near-Earth space conditions driven by an upstream solar wind. We show how to achieve both fast deterministic forecasts and, by using a generative model, produce ensembles to capture forecast uncertainty. This work demonstrates that machine learning offers a way to add uncertainty quantification capability to existing space weather prediction systems, and make hybrid-Vlasov simulation tractable for operational use.

Problem

• Hybrid-Vlasov space weather models are computationally expensive for real-time forecasting
• Current operational systems lack physical realism and uncertainty quantification capabilities
• Need fast, accurate predictions of near-Earth space conditions driven by solar wind

Innovation

• Graph-based neural emulator for space weather prediction
• Autoregressive forecasting using upstream solar wind data
• Generative model ensemble for uncertainty quantification