J.B. (Jochen) Stiasny
J.B. (Jochen) Stiasny
Profile
Jochen received his Ph.D. degree in Wind and Energy Systems from the Technical University of Denmark (DTU) on the topic of Physics-Informed Neural Networks for power system dynamics. He completed his master's degree at ETH Zurich.
Jochen's research interest lies in exploring how machine learning methods can assist in the operation of the grid in light of the onging energy transition. In particular, he seeks for approaches that integrate the existing domain knowledge about this networked, complex dynamical system into the learning algorithms.
Expertise
Publications
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2024
PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks
Jochen Stiasny / Baosen Zhang / Spyros Chatzivasileiadis
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2023
Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems
Simon Stock / Jochen Stiasny / Davood Babazadeh / Christian Becker / Spyros Chatzivasileiadis
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2023
Physics-Informed Neural Networks for Power System Dynamics
Jochen Stiasny
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2023-11
Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility
Jochen Stiasny / Spyros Chatzivasileiadis
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2022
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
Samuel Chevalier / Jochen Stiasny / Spyros Chatzivasileiadis
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Ancillary activities
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2024-01-01 - 2024-12-31