Prediction of Cylindrical Deformation Response Subjected to Underwater Explosion Based on a PointNet Conditional Diffusion Model

• • The proposed PointNet conditional diffusion model with KNN and GNN achieves an MSE of 0.0077 mm², RMSE of 0.0877 mm, MAE of 0.0548 mm, and R² of 0.9858 on the validation set, demonstrating high fidelity in predicting cylindrical shell deformation under underwater explosion. • • Ablation studies show that adding KNN reduces MSE from 0.2753 mm² to 0.0227 mm² (a 91.8% reduction), and further adding GNN reduces MSE to 0.0077 mm² (a 97.2% reduction from baseline), highlighting the critical role of local neighborhood and residual correction in enhancing spatial continuity and prediction accuracy. • • The model accurately predicts center-point deformation displacements on the blast-facing surface across different charge masses (5–1200 g) and standoff distances (80–1000 mm), with errors ranging from 2.33% to 8.12%, confirming its robustness across a wide range of loading conditions. • • The integration of spatial interpolation enables reconstruction of the complete cylindrical surface deformation from sparse observation points, providing a practical tool for visualizing deformation history and final damage patterns, which is essential for underwater platform vulnerability assessment.