发表机构
University College Dublin（都柏林大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出多域XPINN框架，通过将时间域划分为时滞整数倍子域并逐步训练，实现对含再生时滞与非光滑摩擦的加工动力学DDEs的准确参数估计，优于通用PINN且对噪声鲁棒。
AI 中文摘要
本文开发了一种多域扩展物理信息神经网络（XPINN）框架，用于处理非光滑时滞微分方程（DDEs）。这是首次实现将时间域划分为特征时间延迟整数倍的子域，并逐步训练相关子网络同时冻结先前学习参数的方法。通过一个包含再生效应和非光滑摩擦效应的加工动力学模型，验证了所提框架的有效性。结果表明，与通用PINN（SPINN）公式相比，所提出的多域XPINN框架在DDEs中实现了更好的解重构和更优的参数估计。该方法在扩展时间域和非恒定历史函数情况下尤其有效。此外，还使用受高斯测量噪声污染的参考数据评估了逆XPINN（I-XPINN）的鲁棒性。结果表明，I-XPINN对测量噪声保持弹性，物理信息约束引导网络准确恢复底层动力学。这首次证明了所提框架在具有非光滑性和大时间延迟的DDEs中进行可靠参数识别的潜力。
英文摘要
A multi-domain eXtended Physics-Informed Neural Network (XPINN) framework is developed for nonsmooth Delay Differential Equations (DDEs). This is the first implementation to demonstrate the efficacy of partitioning the temporal domain into subdomains of integer multiples of the characteristic time delay and progressively training the associated subnetworks while freezing previously learned parameters. The efficacy of the proposed framework is demonstrated using a machining dynamics model that incorporates both regenerative and nonsmooth frictional effects. Results demonstrate that the proposed multi-domain XPINN framework leads to better solution reconstruction in DDEs and improved parameter estimation compared to a generic PINN (SPINN) formulation. The proposed method works particularly well for extended temporal domains and non-constant history functions. The robustness of inverse XPINN (I-XPINN) is also assessed using reference data contaminated with Gaussian measurement noise. Results indicate that I-XPINN remains resilient to measurement noise and the physics-informed constraints guide the network toward accurately recovering the underlying dynamics. This demonstrates, for the first time, the potential of the proposed framework for reliable parameter identification in DDEs characterised by nonsmoothness and large time delays.
