用物理模型+神经网络，从输入输出数据中识别复杂系统的子模块动态。
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
- 基于端口哈密顿结构，仅用输入输出数据学习子系统动力学。
- 在多物理场系统上验证，可有效处理测量噪声并重构子系统。
- 适合需要模块化建模的工程系统，如机器人、电网等场景。
端口哈密顿神经网络（pHNNs）正成为融合物理规律与深度学习的强大建模工具。现有研究多聚焦于对完整互联系统的建模，而忽视了在整体系统中识别并建模单个子系统的能力。本文提出一种新方法，仅依赖输入输出观测数据，利用端口哈密顿系统的固有可组合性，实现对子系统动态的识别与建模，无需访问内部状态。通过采用输出误差（OE）模型结构，该方法能有效应对测量噪声。在多个互联系统，包括多物理场场景中的测试表明，该方法具备准确识别子系统动态的能力，并可将其无缝集成至新的互联模型中，展现出良好的应用潜力。
原文摘要 · Abstract (English)
Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the potential for identifying and modeling individual subsystems while operating as part of a larger system has been overlooked. This study addresses this gap by introducing a novel method for using pHNNs to identify such subsystems based solely on input-output measurements. By utilizing the inherent compositional property of the port-Hamiltonian systems, we developed an algorithm that learns the dynamics of individual subsystems, without requiring direct access to their internal states. On top of that, by choosing an output error (OE) model structure, we have been able to handle measurement noise effectively. The effectiveness of the proposed approach is demonstrated through tests on interconnected systems, including multi-physics scenarios, demonstrating its potential for identifying subsystem dynamics and facilitating their integration into new interconnected models.
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