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

TL;DR

This paper introduces GNSS, a graph neural network framework for efficient and accurate surrogate modeling of dynamic structural problems, outperforming traditional methods in speed and fidelity.

Contribution

The paper presents GNSS, a novel GNN-based structural simulator designed specifically for dynamic problems, incorporating features like local frame kinematics and wavelength-informed graph construction.

Findings

GNSS accurately simulates dynamic structural behavior over hundreds of timesteps.

GNSS generalizes well to unseen loading conditions where other GNNs fail.

GNSS achieves significant inference speedups compared to finite element methods.

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.