We present a novel physics-informed system identification method to construct a passive linear time-invariant system. In more detail, for a given quadratic energy functional, measurements of the input, state, and output of a system in the time domain, we find a realization that approximates the data well while guaranteeing that the energy functional satisfies a dissipation inequality. To this end, we use the framework of port-Hamiltonian (pH) systems and modify the dynamic mode decomposition to be feasible for continuous-time pH systems. We propose an iterative numerical method to solve the corresponding least-squares minimization problem. We construct an effective initialization of the algorithm by studying the least-squares problem in a weighted norm, for which we present the analytical minimum-norm solution. The efficiency of the proposed method is demonstrated with several numerical examples.

翻译：我们提出了一个新型的物理知情系统识别方法,用于构建一个被动线性时间变量系统。更详细地说,对于特定二次能量功能、对时间领域系统输入、状态和输出的测量,我们发现一个认识,它非常接近数据,同时保证能源功能能满足消散的不平等。为此,我们使用港口-Hamiltonian(pH)系统框架,并修改动态模式分解为连续时间pH系统的可行性。我们提出了一个迭代数字方法,以解决相应的最小方位最小化问题。我们通过在加权规范中研究最小方位问题来构建一种有效的算法初始化,为此我们提出了分析最低规范解决方案。我们用多个数字例子来展示了拟议方法的效率。