# Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate

arXiv: https://arxiv.org/abs/2405.06827 · HTML: https://mlsanity.com/paper/2405.06827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2024.

## Built on by (most cited first, 2 of 2)

- [Operator Learning for Power Systems Simulation](https://mlsanity.com/paper/2510.09704/) (2025)
- [Tools to Explain Neural Networks for Power System Dynamics](https://mlsanity.com/paper/2608.08048/) (2026)

## Abstract (arXiv)

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.