Physics-informed neural network model for transient thermal analysis of superconductors

Abstract Thermal stresses in superconducting tapes, resulting from quench, over-current conditions, or high-current faults, can rapidly elevate temperatures, potentially causing hot spots, performance degradation, localized damage, or catastrophic burnout failure if heat dissipation is inadequately managed. Traditional numerical approaches, such as finite element analysis (FEA), accurately simulate these transient thermal behaviors but suffer from significant computational burdens, complex meshing processes, and sensitivity to input and meshing parameters. Moreover, FEA results often lack generalization capability, necessitating new simulations for each variation in problem parameters. Recently, artificial intelligence (AI) and machine learning (ML) methods have emerged as alternatives for addressing superconductivity problems. However, existing AI/ML models typically operate as black boxes, providing little insight into the physics-based relationships between inputs and outputs. To address this limitation, this study introduces a physics-informed neural network (PINN) framework specifically designed to model transient thermal behaviors in superconducting tapes. By embedding governing physics laws directly into the neural network’s training process, PINNs offer increased transparency, accuracy, and interpretability compared to conventional AI/ML models. This paper, for the first time, aims to demonstrate PINNs as an innovative modeling approach for superconductors, focusing explicitly on transient thermal responses following a heat pulse in superconducting tapes. Validation against analytical solutions and benchmark FEA simulations confirms the PINN model’s effectiveness in accurately capturing sharp thermal gradients and transient heat transfer. Furthermore, results highlight the model’s excellent generalization and extrapolation capabilities, illustrating that a trained PINN can reliably predict scenarios beyond its original training dataset, thereby offering significant potential for tackling complex challenges related to superconductors.