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.

翻译：端口哈密顿神经网络（pHNNs）作为一种融合物理定律与深度学习技术的强大建模工具正日益兴起。尽管大多数研究集中于对互联系统的整体动力学进行建模，但识别和建模作为更大系统一部分运行的独立子系统的潜力却被忽视了。本研究通过引入一种基于端口哈密顿神经网络、仅利用输入-输出测量数据来识别此类子系统的新方法，填补了这一空白。利用端口哈密顿系统固有的组合特性，我们开发了一种能够学习独立子系统动力学的算法，而无需直接访问其内部状态。此外，通过选择输出误差模型结构，我们能够有效地处理测量噪声。所提方法的有效性通过对互联系统（包括多物理场场景）的测试得到了验证，展示了其在识别子系统动力学并促进其集成到新互联模型中的潜力。