A Physics-Stabilized Self-Updating Digital Twin Framework Using Physics-Informed Neural Networks for Thermal Field Prediction

Digital twins increasingly rely on autonomous self-updating mechanisms to remain synchronized with physical systems; however, repeated self-updating can lead to error accumulation, numerical instability, and progressive loss of physical consistency when models iteratively learn from their own predictions. To address this challenge, the study proposes a physics-stabilized self-updating digital twin framework based on Physics-Informed Neural Networks (PINNs) and demonstrates its core principles on a canonical thermal field prediction problem. The framework integrates adaptive physics-loss weighting, a physics-only stabilization stage, and second-derivative smoothness regularization within the self-updating loop, enabling controlled data assimilation while explicitly enforcing governing equation constraints. Numerical results show a monotonic reduction in root mean square error (RMSE) from approximately 1 × 10⁻³ in the first update cycle to 8 × 10⁻⁵ after four update cycles, accompanied by effective suppression of model drift and a substantial reduction in partial differential equation (PDE) residuals compared to a naïve self-updating strategy. Furthermore, the analysis reveals the existence of an update saturation point, beyond which additional autonomous updates yield diminishing accuracy improvements, providing a physically motivated stopping criterion for autonomous updating. By establishing a stable and physically interpretable self-updating architecture, the study provides a foundational framework for the development of reliable digital twins, with clear potential for extension to more complex thermal and Multiphysics systems.