1 citations · 1 across the 4 of their papers we have counts for
5 papers
$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
Júlia Vicens Figueres, Juliette Vanderhaeghen, Federica Bragone +2
Physics-Informed Neural Networks (PINNs) are a novel computational approach for solving partial differential equations (PDEs) with noisy and sparse initial and boundary data. Altho…
Discovering Partially Known Ordinary Differential Equations: a Case Study on the Chemical Kinetics of Cellulose Degradation
Federica Bragone, Kateryna Morozovska, Tor Laneryd +2
The degree of polymerization (DP) is one of the methods for estimating the aging of the polymer based insulation systems, such as cellulose insulation in power components. The main…
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…