# PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks

arXiv: https://arxiv.org/abs/2303.10256 · HTML: https://mlsanity.com/paper/2303.10256/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [Constructing Neural Network-Based Models for Simulating Dynamical Systems](https://mlsanity.com/paper/2111.01495/) (2021)
- [DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks](https://mlsanity.com/paper/2109.04304/) (2021)
- [On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators](https://mlsanity.com/paper/2301.12538/) (2023)
- [Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility](https://mlsanity.com/paper/2303.08994/) (2023)
- [A Frequency Domain Approach to Predict Power System Transients](https://mlsanity.com/paper/2111.01103/) (2021)
- [Solving Power System Differential Algebraic Equations Using Differential Transformation](https://mlsanity.com/paper/1903.00935/) (2019)
- [A Time-Power Series Based Semi-Analytical Approach for Power System Simulation](https://mlsanity.com/paper/1804.07330/) (2018)

## Built on by (most cited first, 9 of 9)

- [Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators](https://mlsanity.com/paper/2410.24162/) (2024)
- [Error estimation for physics-informed neural networks with implicit Runge-Kutta methods](https://mlsanity.com/paper/2401.05211/) (2024)
- [Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations](https://mlsanity.com/paper/2510.10483/) (2025)
- [Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation](https://mlsanity.com/paper/2603.17836/) (2026)
- [Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems](https://mlsanity.com/paper/2604.19930/) (2026)
- [Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics](https://mlsanity.com/paper/2410.07527/) (2024)
- [Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks](https://mlsanity.com/paper/2502.00552/) (2025)
- [Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE](https://mlsanity.com/paper/2512.23624/) (2025)
- [Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model](https://mlsanity.com/paper/2610.20462/) (2026)

## Abstract (arXiv)

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.