Jochen Stiasny

According to our database, Jochen Stiasny authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Correctness Verification of Neural Networks Approximating Differential Equations.

Petros Ellinas, Rahul Nellikkath, Ignasi Ventura, Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 2024

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods.

Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 2024

2023

Solving Differential-Algebraic Equations in Power Systems Dynamics with Neural Networks and Spatial Decomposition.

Jochen Stiasny, Spyros Chatzivasileiadis, Baosen Zhang

CoRR, 2023

Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility.

Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 2023

2022

Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems.

Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis

CoRR, 2022

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations.

Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos

CoRR, 2022

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems.

Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis

CoRR, 2022

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers.

Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis

Proceedings of the Learning for Dynamics and Control Conference, 2022

2021

Transient Stability Analysis with Physics-Informed Neural Networks.

Jochen Stiasny, Georgios S. Misyris, Spyros Chatzivasileiadis

CoRR, 2021

Contracting Neural-Newton Solver.

Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 2021

Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation.

Jochen Stiasny, Samuel Chevalier, Spyros Chatzivasileiadis

Proceedings of the IEEE International Conference on Communications, 2021

Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization.

Georgios S. Misyris, Jochen Stiasny, Spyros Chatzivasileiadis

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics.

Jochen Stiasny, George S. Misyris, Spyros Chatzivasileiadis

CoRR, 2020