The smooth interaction between the pantograph and the catenary is crucial for the operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary system (PCS) constructed based on physical principles are important tools for analyzing their interactions; however, these models rely on accurate system parameters (such as stiffness, damping, and mass). Under actual operating conditions, the system parameters of the PCS exhibit time-varying characteristics and are difficult to measure, making it challenging for dynamic models to accurately represent the system’s behavior. Data-driven intelligent algorithms, with powerful feature extraction and nonlinear fitting capabilities, provide new approaches for solving system response prediction and state identification problems of the PCS. However, excessive reliance on large amounts of data for training may lead to poor generalization ability and pose challenges to model robustness, such as sensitivity to input noise or outliers. To address these issues, this paper proposes a surrogate model for the interaction of the PCS by integrating physical information with deep neural networks. The model introduces a novel neural operator that combines Transformers and convolutions (Convs), capable of capturing complex mapping relationships among various parameters within the dynamic model of the PCS in the frequency domain. A residual network incorporating physical information is designed to simulate the intricate correlations among system parameters. Additionally, a dynamic weighting balance algorithm is proposed to adjust the losses of different physical equations dynamically, ensuring the balance of physical information during training. The proposed model effectively performs response prediction and state identification of the PCS. It demonstrates excellent performance on both simulation and real-world data, providing new insights and methodologies for studying PCS interactions.

中文翻译：

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受电弓-接触网系统相互作用的代理建模


受电弓和接触网之间的平稳交互对于铁路车辆的运行安全至关重要。基于物理原理构建的受电弓-接触网系统 （PCS） 耦合动力学模型是分析其相互作用的重要工具;但是，这些模型依赖于精确的系统参数（例如刚度、阻尼和质量）。在实际操作条件下，PCS 的系统参数表现出时变特性并且难以测量，这使得动态模型难以准确表示系统的行为。数据驱动的智能算法，具有强大的特征提取和非线性拟合能力，为解决 PCS 的系统响应预测和状态识别问题提供了新的方法。然而，过度依赖大量数据进行训练可能会导致泛化能力差，并对模型鲁棒性构成挑战，例如对输入噪声或异常值的敏感性。为了解决这些问题，本文通过将物理信息与深度神经网络集成，提出了一种 PCS 交互的代理模型。该模型引入了一种新颖的神经算子，它结合了 Transformer 和卷积 （Convs），能够在频域中捕获 PCS 动态模型中各种参数之间的复杂映射关系。设计了一个包含物理信息的残差网络，以模拟系统参数之间错综复杂的相关性。此外，该文还提出了一种动态加权平衡算法，以动态调整不同物理方程的损失，保证了训练过程中物理信息的平衡。 所提出的模型有效地执行了 PCS 的响应预测和状态识别。它在仿真和真实数据上都表现出出色的性能，为研究 PCS 交互提供了新的见解和方法。