Jochen Stiasny
Jochen Stiasny
Postdoc, Intelligent Electrical Power Grids, Delft University of Technology
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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
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
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
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
Transient stability analysis with physics-informed neural networks
J Stiasny, GS Misyris, S Chatzivasileiadis
arXiv preprint arXiv:2106.13638, 2021
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
J Stiasny, S Chevalier, R Nellikkath, B Sævarsson, S Chatzivasileiadis
arXiv preprint arXiv:2203.07505, 2022
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
Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility
J Stiasny, S Chatzivasileiadis
Electric Power Systems Research 224, 109748, 2023
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
Solving Differential-Algebraic Equations in Power Systems Dynamics with Neural Networks and Spatial Decomposition
J Stiasny, S Chatzivasileiadis, B Zhang
arXiv preprint arXiv:2303.10256, 2023
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
Interpretable machine learning for power systems: establishing confidence in SHapley Additive exPlanations
RI Hamilton, J Stiasny, T Ahmad, S Chevalier, R Nellikkath, ...
arXiv preprint arXiv:2209.05793, 2022
Correctness Verification of Neural Networks Approximating Differential Equations
P Ellinas, R Nellikath, I Ventura, J Stiasny, S Chatzivasileiadis
arXiv preprint arXiv:2402.07621, 2024
Physics-Informed Neural Networks for Power System Dynamics
J Stiasny
Technical University of Denmark, 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
Contracting Neural-Newton Solver
S Chevalier, J Stiasny, S Chatzivasileiadis
arXiv preprint arXiv:2106.02543, 2021
Integrating Physics-Informed Neural Networks into Power System Dynamic Simulations
IV Nadal, J Stiasny, S Chatzivasileiadis
arXiv preprint arXiv:2404.13325, 2024
Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
J Stiasny, S Chatzivasileiadis
arXiv preprint arXiv:2401.05211, 2024
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