  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, respectively operator inference, 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.
