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This study introduces a Joint Initialization Physics-Informed Neural Network (JI-PINN) that optimizes the effective multiplication factor (keff) and neutron flux network parameters simultaneously using a low-resolution approximate solution to the K-eigenvalue problem. By incorporating physical constraints during the joint optimization process, the method significantly enhances computational efficiency while maintaining solution accuracy across various benchmark cases. The results demonstrate a reduction in computational time by up to 49.4%, alongside a decrease in anomalous keff results, showcasing JI-PINN's robustness in neutron diffusion problems.
Achieving up to 49.4% faster computations for neutron diffusion problems without sacrificing accuracy could revolutionize reactor core analysis.
Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-informed neural networks (PINNs) incorporate neutron diffusion equations and boundary conditions into network training to efficiently determine the neutron flux distribution and keff. To further improve the efficiency of keff calculations using PINNs, a Joint Initialization Physics-Informed Neural Network (JI-PINN) is proposed in this work. In this method, a low-resolution approximate solution to the K-eigenvalue problem is used to construct a joint initial state for the flux network parameters and keff, and both are then jointly optimized under physical constraints. The proposed method was validated on a two-dimensional two-group two-material case, the IAEA 2D benchmark, a two-dimensional two-group four-material case, and a three-dimensional single-group case. For these test cases, the total computational time was reduced by 25.4%, 38.2%, 49.4%, and 28.9%, respectively, while comparable solution accuracy was maintained. The occurrence of anomalous results associated with marked deviations of keff from the reference value was also reduced. The proposed method provides a more efficient and robust initialization strategy for solving neutron diffusion K-eigenvalue problem with PINNs.
