"Supplementary Material for \u201cPGI-RUL: Prior-Guided Generative Imputation and Physics-Informed Prediction for Aero-Engine RUL\u201d"

"This supplementary material provides comprehensive extended experimental results and detailed technical analyses to complement the findings presented in the main manuscript. To further validate the proposed PGI-RUL framework, we include additional qualitative visualizations of remaining useful life (RUL) prediction trajectories, specifically focusing on its performance under challenging scenarios characterized by severe data sparsity and high noise levels. Furthermore, a rigorous ablation study is presented to systematically evaluate the contribution and necessity of each key module within the architecture. We also provide a thorough quantitative analysis of model complexity, parameter overhead, and computational efficiency to justify its industrial applicability. These supplementary results are intended to offer deeper insights into the robustness, interpretability, and practical feasibility of the proposed method for complex industrial prognostics and health management applications."