mlsanityData to 14 Oct 2026

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

arXiv 2303.10256 ↗First posted 17 Mar 2023eess.SY cs.LG cs.NAcited by 9

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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.

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

10 of 28 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Automatic differentiation in machine learning: a surveycited by 855
  3. 2019Physics-Informed Neural Networks for Power Systemscited by 80
  4. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 31
  5. 2021DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networkscited by 28
  6. 2023On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulatorscited by 13
  7. 2023Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibilitycited by 13
  8. 2021A Frequency Domain Approach to Predict Power System Transientscited by 6
  9. 2019Solving Power System Differential Algebraic Equations Using Differential Transformationcited by 3
  10. 2018A Time-Power Series Based Semi-Analytical Approach for Power System Simulationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.

  1. 2024Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operatorscited by 5
  2. 2024Error estimation for physics-informed neural networks with implicit Runge-Kutta methodscited by 2
  3. 2025Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equationscited by 1
  4. 2026Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulationcited by 1
  5. 2026Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systemscited by 1
  6. 2024Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamicscited by 0
  7. 2025Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networkscited by 0
  8. 2025Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICEcited by 0
  9. 2026Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Modelcited by 0