Physics-Informed Enhanced Fourier Neural Operator for Solving Pantograph-Catenary Interaction in Electric Railway

In electric railway systems, the interaction performance of pantograph-catenary systems (PCS) is essential for ensuring a stable electrical supply. Establishing high-fidelity numerical models using the finite element method is generally desirable, yet it involves considerable computational complexity and time demands. In this paper, we propose a novel dynamic modelling method that integrates physical information and data-driven approaches to solve the pantograph-catenary interaction, called Physics-Informed Enhanced Fourier Neural Operator (PI-EFNO). Firstly, the enhanced Fourier Neural Operator (EFNO) based on global perceptron is developed to capture the nonlinear mapping between parameter space and output solution space by approximating solutions to dynamic equations in the frequency domain. Then, we integrate multiple physics-informed loss terms into the EFNO architecture to handle implicit constraints within coupled equations, which leverages physical principles to guide learning while reducing the need for labelled training data. Additionally, a dynamic weighting mechanism adaptively balances the contributions of various terms in the physics-based loss function. Experimental results validate the effectiveness and advantages of PI-EFNO in modelling pantograph-catenary dynamics. It demonstrates exceptional accuracy, computational efficiency, and generalizability across diverse pantograph models and catenary conditions, exhibiting strong capability in learning physically coupled equation systems.