  The dominant paradigm for power system dynamic simulation is to build
system-level simulations by combining physics-based models of individual
components. The sheer size of the system along with the rapid integration of
inverter-based resources exacerbates the computational burden of running time
domain simulations. In this paper, we propose a data-driven surrogate model
based on implicit machine learning -- specifically deep equilibrium layers and
neural ordinary differential equations -- to learn a reduced order model of a
portion of the full underlying system. The data-driven surrogate achieves
similar accuracy and reduction in simulation time compared to a physics-based
surrogate, without the constraint of requiring detailed knowledge of the
underlying dynamic models. This work also establishes key requirements needed
to integrate the surrogate into existing simulation workflows; the proposed
surrogate is initialized to a steady state operating point that matches the
power flow solution by design.
