A hybrid global–local computational framework for ship hull structural analysis using homogenized model and graph neural network

This study presents a computational framework for global–local structural analysis of ship hull girders that integrates an equivalent single-layer (ESL) model with a graph neural network (GNN). A coarse-mesh homogenized ESL model efficiently predicts the global displacement field, from which degrees of freedom (DOFs) along stiffened panel boundaries are extracted. A global-to-local DOF mapping and reconstruction procedure is developed to recover detailed boundary kinematics for local analysis. The reconstructed DOFs, together with panel geometry and loading, serve as inputs to a heterogeneous graph transformer (HGT), a subtype of GNN, which rapidly and accurately predicts the detailed stress and displacement fields for any panel within the hull girder. The HGT is trained using high-fidelity 3D panel finite element model with reconstructed boundary conditions, enabling it to generalize across varying panel geometries, loadings, and boundary behaviors. Once trained, the framework requires only the global ESL solution in order to generate detailed local responses, making it highly suitable for optimization. Validation on three box beam case studies demonstrates that the global prediction error is governed by the coarse-mesh ESL solution, while the HGT maintains high local accuracy and clearly outperforms conventional ESL-based stress estimation method. • Hybrid global–local framework combining ESL modeling with a graph neural network. • Coarse-mesh ESL predicts global displacements; DOFs are extracted at panel boundaries. • Global-to-local DOF mapping reconstructs detailed kinematics for local panel analysis. • A heterogeneous graph transformer predicts local stress and displacement fields. • Validated on box-beam cases; HGT outperforms conventional ESL-based stress methods.