| Abstract Scope |
Dynamic systems evolve through intricate interactions where local events impact global behavior, reflecting real-world interconnections. Modeling these systems requires capturing both local and long-range dynamics with accuracy and efficiency, a balance that current mesh-based Graph Neural Network (GNN) methods often struggle to achieve, especially with large datasets and complex meshes. Inspired by real-world dynamics, we introduce the Mesh-based Multi-Segment Graph Network (MMSGN), a framework designed to address these challenges through a hierarchical information exchange mechanism that aligns with physical properties. MMSGN integrates micro-level local interactions with macro-level global exchanges to accurately capture both local and global dynamics while remaining computationally efficient. Our model demonstrates superior accuracy, mesh quality, and scalability on multiple dynamic system datasets, outperforming several state-of-the-art methods and proving well-suited for large-scale, complex system simulations across diverse scenarios. |