2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2026★ 2 cited
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…
cs.LG2025
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…
cs.LG2024
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
Ibai Ramirez, Joel Pino, David Pardo +5
Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer hea…