Enhancing Nuclear Energy Efficiency through NAS-Optimized PINNs for Neutron Physics Equations

Nuclear reactors are crucial for the production and provision of nuclear energy, and their core design can be modeled as neutron diffusion and transport equations. Therefore, the fast and accurate solution of these two kinds of equations can effectively control nuclear reactor to ensure its safety and stability. Recent developments in Physics-Informed Neural Network (PINN) have significantly enhanced computational speed and efficiency in solving partial differential equations. However, their application is still constrained by the inflexibility of predefined structures, which limits its width and depth in practical applications to some extent. This paper proposes an innovative approach utilizing Neural Architecture Search (NAS) to dynamically identify optimal PINN architectures for neutron diffusion and transport equations in nuclear reactors. Specifically, we first introduce differential transform order theory, facilitating the transformation of integral terms in transport equations into higher-order differential terms to adapt the PINN model. Secondly, we use genetic algorithm (GA) as an optimization strategy in NAS to find the PINN model that is most suitable for solving the reactor equation. The verification results prove that the method has higher accuracy in solving reactor equations of different dimensions, providing a more accurate and efficient solution for complex neutron equations.