13 citations · 20 across the 4 of their papers we have counts for
4 papers
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino +2
Physics-Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains…
Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino +3
Scientific Machine Learning (SciML) integrates physics and data into the learning process, offering improved generalization compared with purely data-driven models. Despite its pot…
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugasti
Batteries are a key enabling technology for the decarbonization of transport and energy sectors. The safe and reliable operation of batteries is crucial for battery-powered systems…
A Hybrid Probabilistic Battery Health Management Approach for Robust Inspection Drone Operations
Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugastia +1
Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets tha…