256 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of the Basque CountryES11 papers
- Donostia International Physics CenterES7 papers
- IkerbasqueES7 papers
- IkerlanES5 papers
- GAIKER Technology CentreES4 papers
- Universitat de BarcelonaES4 papers
- Material Physics CenterES3 papers
- Basque Center for Applied MathematicsES2 papers
- Biogipuzkoa Health Research InstituteES2 papers
- CIC nanoGUNEES2 papers
- National Institute of InformaticsJP2 papers
- OsloMet – Oslo Metropolitan UniversityNO2 papers
7 papers · 1 filter
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…
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach
Josu Yeregui, Iker Lopetegi, Sergio Fernandez +2
This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (…
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…
Diagnostic Spatio-temporal Transformer with Faithful Encoding
Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen +2
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extr…
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss
Jokin Labaien, Ekhi Zugasti, Xabier De Carlos
Deep Learning has become a very valuable tool in different fields, and no one doubts the learning capacity of these models. Nevertheless, since Deep Learning models are often seen…
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance
Javier Fernandez-Anakabe, Ekhi Zugasti Uriguen, Urko Zurutuza Ortega
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm t…