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20242026
most citedToolbox for Developing Physics Informed Neural Networks for Power Systems Components

1 citations · 1 across the 8 of their papers we have counts for

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eess.SY2026

RAPTOR: RAndom-projection Physics-informed Transient sOlveR

Petros Ellinas, Benjamin Vilmann, Spyros Chatzivasileiadis +1

The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alo…

eess.SY2026

Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening

Sarra Bouchkati, Petros Ellinas, Adriana Geisler +4

Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical…

eess.SY2026

Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation

Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk +1

Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question…

eess.SY2025

Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components

Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk +1

Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for…

eess.SY2025★ 1 cited

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components

Ioannis Karampinis, Petros Ellinas, Ignasi Ventura Nadal +2

This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing comple…

eess.SY2024

Correctness Verification of Neural Networks Approximating Differential Equations

Petros Ellinas, Rahul Nellikath, Ignasi Ventura +2

Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accele…