论文标题
数据驱动的非刻痕被动系统的哈米尔顿港结构化识别
Data-driven port-Hamiltonian structured identification for non-strictly passive systems
论文作者
论文摘要
在这项工作中，我们详细介绍了一个程序，以基于频域数据构建减少订单模型，该模型保留了非刻板的被动性能和港口 - 哈米尔顿港结构。提出的方案基于Benner等。 （2020年），已改编（i）处理非刻痕的被动模型，以及（ii）处理在复杂配置上应用Loewner框架时观察到的数值问题。我们在非常复杂的二维波方程上验证了所提出的方案，为此，离散的版本保留了港口 - 哈米尔顿纳派形式。
In this work, we detail a procedure to construct a reduced order model on the basis of frequency-domain data, that preserves the non-strictly passive property and the port-Hamiltonian structure. The proposed scheme is based on Benner et al. (2020) contribution, which has been adapted (i) to handle non-strictly passive model, and (ii) to handle numerical issues observed when applying the Loewner framework on complex configurations. We validate the proposed scheme on a very complex two-dimensional wave equation, for which the discretized version preserves the port-Hamiltoninan form.
