A multi-physics-informed neural network framework for dual prediction of creep residual life and damage fraction in austenitic stainless steels

Accurate prediction of creep degradation across multiple high-temperature alloy grades remains a significant challenge in structural integrity assessment. Classical empirical and mechanistic models are typically restricted to estimating total creep rupture life and are often material-specific, limiting their generalizability. This study presents a physics-informed neural network (PINN) framework tailored for creep life assessment, enabling simultaneous prediction of creep residual life (CRL) and creep damage fraction (CDF) in nine grades of Austenitic Stainless Steels (AusSS). The scientific contributions are threefold. First, a unified dual-output modeling strategy is introduced to concurrently predict residual life and accumulated damage, enabling integrated failure prognosis beyond classical rupture-life models. Second, a multi-physics-informed feature engineering approach is developed by incorporating physically derived descriptors, including the Larson–Miller parameter (LMP), Monkman–Grant parameter (MGP), and stacking fault energy (SFE), thereby embedding creep mechanics directly into the learning process. Third, a physics-constrained loss formulation is implemented to enforce domain-consistent bounds (CRL > 0 and 0 ≤ CDF ≤ 1), ensuring physically admissible and reliable predictions. The framework is trained and validated on 1179 datapoints spanning diverse compositions and service conditions. The proposed PINN outperforms conventional Machine Learning (ML) and standard deep neural network models, achieving test R² values of 0.933 for CRL and 0.936 for CDF. SHAP-based analysis further confirms the dominant role of physics-informed features. The model provides a generalizable and interpretable tool for high-temperature structural health monitoring and predictive maintenance.