Jochen Stiasny

According to our database, Jochen Stiasny authored at least 19 papers between 2020 and 2026.

Collaborative distances:

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

2026

Sensitivity Quantification for Distribution System State Estimation.

Betul Mamudi, Jochen Stiasny, Jochen L. Cremer

CoRR, May, 2026

A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting.

Runyao Yu, Julia Lin, Derek W. Bunn, Jochen Stiasny, Wentao Wang, Yujie Chen, Tara Esterl, Peter Palensky, Jochen L. Cremer

CoRR, May, 2026

Residual Power Flow for Neural Solvers.

Jochen Stiasny, Jochen L. Cremer

CoRR, January, 2026

2025

PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting.

Runyao Yu, Chenhui Gu, Jochen Stiasny, Qingsong Wen, Wasim Sarwar Dilov, Lianlian Qi, Jochen L. Cremer

CoRR, August, 2025

Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids.

Shengyuan Yan, Farzad Vazinram, Zeynab Kaseb, Lindsay Spoor, Jochen Stiasny, Betul Mamudi, Amirhossein Heydarian Ardakani, Ugochukwu Orji, Pedro P. Vergara, Yu Xiang, Jerry Guo

CoRR, April, 2025

2024

Integrating Physics-Informed Neural Networks into Power System Dynamic Simulations.

Ignasi Ventura Nadal, Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 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