Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics

2025-10-29

AI Summary

Existing graph neural networks (GNNs) have been employed as surrogate models in computational fluid dynamics, yet their application to dynamic structural mechanics—particularly wave-dominated problems—remains unexplored. This work introduces GNSS, the first GNN-based surrogate for dynamic structural simulation. Methodologically, GNSS features: (1) node-fixed local coordinate systems to eliminate numerical cancellation in velocity estimation; (2) a sign-aware loss function to suppress phase drift during long-horizon rollout; and (3) wavelength-aware graph connectivity to optimize topological structure. Built upon an encode-process-decode architecture, it integrates finite-difference-based velocity estimation with physics-informed graph construction. Evaluated on a 50-kHz pulsed excitation beam benchmark, GNSS achieves hundreds of high-fidelity time steps with strong generalization to unseen dynamics. It outperforms explicit finite-element solvers significantly in inference speed while preserving spatiotemporal accuracy.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little attention has been given to structural problems, especially for dynamic cases. To address this gap, we introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems. GNSS follows the encode-process-decode paradigm typical of GNN-based machine learning models, and its design makes it particularly suited for dynamic simulations thanks to three key features: (i) expressing node kinematics in node-fixed local frames, which avoids catastrophic cancellation in finite-difference velocities; (ii) employing a sign-aware regression loss, which reduces phase errors in long rollouts; and (iii) using a wavelength-informed connectivity radius, which optimizes graph construction. We evaluate GNSS on a case study involving a beam excited by a 50kHz Hanning-modulated pulse. The results show that GNSS accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions. Compared with explicit finite element baselines, GNSS achieves substantial inference speedups while preserving spatial and temporal fidelity. These findings demonstrate that locality-preserving GNNs with physics-consistent update rules are a competitive alternative for dynamic, wave-dominated structural simulations.

Problem

• Developing GNN surrogate models for dynamic structural simulations
• Addressing phase errors in long-term structural dynamics predictions
• Optimizing graph construction for wave-dominated structural problems

Innovation

• Uses node-fixed frames to avoid velocity errors
• Employs sign-aware loss to reduce phase errors
• Applies wavelength-informed connectivity for graph optimization