| Physics-informed neural networks for non-linear system identification for power system dynamics J Stiasny, GS Misyris, S Chatzivasileiadis 2021 IEEE Madrid PowerTech, 1-6, 2021 | 63 | 2021 |
| Sensitivity analysis of electric vehicle impact on low-voltage distribution grids J Stiasny, T Zufferey, G Pareschi, D Toffanin, G Hug, K Boulouchos Electric Power Systems Research 191, 106696, 2021 | 59 | 2021 |
| Machine Learning in Power Systems: Is It Time to Trust It? S Chatzivasileiadis, A Venzke, J Stiasny, G Misyris IEEE Power and Energy Magazine 20 (3), 32-41, 2022 | 36 | 2022 |
| Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation J Stiasny, S Chevalier, S Chatzivasileiadis 2021 IEEE International Conference on Communications, Control, and Computing …, 2021 | 32 | 2021 |
| Capturing power system dynamics by physics-informed neural networks and optimization GS Misyris, J Stiasny, S Chatzivasileiadis 2021 60th IEEE Conference on Decision and Control (CDC), 4418-4423, 2021 | 20 | 2021 |
| Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems J Stiasny, S Chevalier, R Nellikkath, B Sævarsson, S Chatzivasileiadis Proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium …, 2022 | 19 | 2022 |
| Transient stability analysis with physics-informed neural networks J Stiasny, GS Misyris, S Chatzivasileiadis arXiv preprint arXiv:2106.13638, 2021 | 17 | 2021 |
| Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility J Stiasny, S Chatzivasileiadis Electric Power Systems Research 224, 109748, 2023 | 16 | 2023 |
| PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks J Stiasny, B Zhang, S Chatzivasileiadis Electric Power Systems Research 235, 110796, 2024 | 15* | 2024 |
| Bayesian physics-informed neural networks for robust system identification of power systems S Stock, J Stiasny, D Babazadeh, C Becker, S Chatzivasileiadis 2023 IEEE Belgrade PowerTech, 1-6, 2023 | 11 | 2023 |
| Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers S Chevalier, J Stiasny, S Chatzivasileiadis Learning for Dynamics and Control Conference, 803-816, 2022 | 3* | 2022 |
| Sensitivity analysis of EV impact on distribution grids based on Monte-Carlo simulations J Stiasny, T Zufferey, G Pareschi, D Toffanin, G Hug, K Boulouchos Master Thesis, ETH Zurich, 2019 | 3 | 2019 |
| Correctness Verification of Neural Networks Approximating Differential Equations P Ellinas, R Nellikath, I Ventura, J Stiasny, S Chatzivasileiadis arXiv preprint arXiv:2402.07621, 2024 | 2 | 2024 |
| Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations T Ahmad, RI Hamilton, J Stiasny, S Chevalier, R Nellikkath, ... ICLR 2024 Workshop: Tackling Climate Change with Machine Learning, 2024 | 2* | 2024 |
| Integrating Physics-Informed Neural Networks into Power System Dynamic Simulations IV Nadal, J Stiasny, S Chatzivasileiadis arXiv preprint arXiv:2404.13325, 2024 | 1 | 2024 |
| Physics-Informed Neural Networks for Power System Dynamics J Stiasny Technical University of Denmark, 2023 | 1 | 2023 |
| Error estimation for physics-informed neural networks with implicit Runge-Kutta methods J Stiasny, S Chatzivasileiadis arXiv preprint arXiv:2401.05211, 2024 | | 2024 |
Spyros ChatzivasileiadisProfessor, Head of Section, Technical University of Denmark (DTU)Adresse e-mail validée de dtu.dk
