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[2602.16213] Graph neural network for colliding particles with an application to sea ice floe modeling
Summary
This article presents a novel Graph Neural Network approach for modeling sea ice dynamics, focusing on particle collisions and data assimilation techniques to enhance simulation efficiency.
Why It Matters
The research addresses the limitations of traditional numerical methods in sea ice modeling, offering a scalable and efficient alternative through machine learning. This is particularly relevant as climate change impacts polar regions, necessitating improved forecasting tools for environmental monitoring.
Key Takeaways
- Introduces a Graph Neural Network model for sea ice dynamics.
- Utilizes data assimilation techniques to enhance predictive accuracy.
- Demonstrates improved simulation efficiency compared to traditional methods.
- Validates the model with synthetic data, ensuring reliability.
- Highlights the potential of machine learning in environmental modeling.
Computer Science > Machine Learning arXiv:2602.16213 (cs) [Submitted on 18 Feb 2026] Title:Graph neural network for colliding particles with an application to sea ice floe modeling Authors:Ruibiao Zhu View a PDF of the paper titled Graph neural network for colliding particles with an application to sea ice floe modeling, by Ruibiao Zhu View PDF Abstract:This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the simulation of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision a...
