1 citations · 1 across the 2 of their papers we have counts for
3 papers
Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
Sirui Li, Federica Bragone, Matthieu Barreau +2
Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then u…
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation
Francis Tembo, Federica Bragone, Tor Laneryd +2
Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. T…
MILP initialization for solving parabolic PDEs with PINNs
Sirui Li, Federica Bragone, Matthieu Barreau +1
Physics-Informed Neural Networks (PINNs) are a powerful deep learning method capable of providing solutions and parameter estimations of physical systems. Given the complexity of t…