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
46 papers
Optical forces, helicity, angular momentum and how they are all intertwined
Iker Gómez-Viloria, Enrique Ayllón García, Jorge Olmos-Trigo +4
The theoretical description of optical forces and torques on micron_sized particles is a crucial area of research and has formed the foundation for advancements in optical trapping…
Characterizing the Backscattered Spectrum of Mie Spheres
Martín Molezuelas-Ferreras, Álvaro Nodar, María Barra-Burillo +10
This study describes both experimentally and theoretically an important hitherto undiscovered feature of the scattering of micron_sized spherical objects when illuminated with high…
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…
Search-based Generation of Waypoints for Triggering Self-Adaptations in Maritime Autonomous Vessels
Karoline Nylænder, Aitor Arrieta, Shaukat Ali +1
Self-adaptation in maritime autonomous vessels (AVs) enables them to adapt their behaviors to address unexpected situations while maintaining dependability requirements. During the…
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 (…
Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring
Adrian Villalobos, Iban Barrutia, Rafael Pena-Alzola +2
Semiconductor devices, especially MOSFETs (Metal-oxide-semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by aging processe…
A Useful Metric for the NISQ Era: Qubit Error Probability and Its Role in Zero Noise Extrapolation
Nahual Sobrino, Unai Aseginolaza, Joaquim Jornet-Somoza +1
Accurate assessment and management of errors is indispensable for extracting useful results from noisy intermediate-scale quantum (NISQ) devices. In this work, we propose the qubit…
Efficient Calculation of Stabilization Parameters in RF Power Amplifiers
Libe Mori, Ibone Lizarraga, Aitziber Anakabe +3
This paper proposes an efficient method for the calculation of the stabilization parameters in RF power amplifiers operating in periodic large-signal regimes. Stabilization is achi…
Monitoring lead-acid battery function using operando neutron radiography
Jose Miguel Campillo-Robles, Damian Goonetilleke, Daniel Soler +6
Investigating batteries while they operate allows researchers to track the inner electrochemical processes involved in working conditions. This study describes the first neutron ra…
General hybrid asymmetric capacitor model: validation with a commercial lithium ion capacitor
Jose Miguel Campillo-Robles, Xabier Artetxe, Karmele del Teso Sánchez +5
Modelling and numerical simulations play a vital role in the design and optimization of electrochemical energy storage devices. In this study, a general physics-based model is deve…
Effect of thickness on the maximum potential drop of current collectors
Jose Miguel Campillo-Robles, Xabier Artetxe, Karmele del Teso Sánchez
The basic principle for achieving high-power capability on an electrochemical energy storage cell is minimizing the overall resistance. The resistance due to current collecting sys…
Review and analysis of thermophysical properties of a sulfuric acid-water electrolyte
Laura Oca, Jose Miguel Campillo, Mounir M. Bou-Ali
In this work, we have performed a critical review of the thermophysical properties of sulfuric acid-water mixtures. The thermophysical properties analyzed are density, viscosity, r…
Foundation Models for the Digital Twin Creation of Cyber-Physical Systems
Shaukat Ali, Paolo Arcaini, Aitor Arrieta
Foundation models are trained on a large amount of data to learn generic patterns. Consequently, these models can be used and fine-tuned for various purposes. Naturally, studying s…
Uncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studies
Jone Ugarte-Valdivielso, Jose I. Aizpurua, Manex Barrenetxea-Iñarra
Transformers are one of the key assets in AC distribution grids and renewable power integration. During transformer energization inrush currents appear, which lead to transformer d…
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…
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…
Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
Yingbai Hu, Fares J. Abu-Dakka, Fei Chen +4
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitatin…
Sensitiveness of the ratio between monovacancy and bulk positron lifetimes to the approximations used in the calculations: Periodic behaviour
J. M. Campillo-Robles, E. Ogando, F. Plazaola
Positron lifetimes have been calculated in bulk and monovacancies for most of the elements of the periodic table. Self-consistent and non-self-consistent schemes have been used for…
Positron lifetime calculation of the elements of the periodic table
J. M. Campillo Robles, E. Ogando, F. Plazaola
Theoretical positron lifetime values have been calculated systematically for most of the elements of the Periodic Table. Self-consistent and non-self-consistent schemes have been u…
Positron lifetime calculations for defects in Zn
J. M. Campillo, F. Plazaola, N. de Diego
The effect of the lattice relaxation at vacancy clusters and interstitial-type dislocation loops on the lifetime of positrons in Zn has been studied. Defective relaxed structures h…
GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming
Jon Ayerdi, Valerio Terragni, Gunel Jahangirova +2
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying pr…
Exploring the transformation of user interactions to Adaptive Human-Machine Interfaces
Angela Carrera-Rivera, Daniel Reguera-Bakhache, Felix Larrinaga +1
Human-machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often st…
MarMot: Metamorphic Runtime Monitoring of Autonomous Driving Systems
Jon Ayerdi, Asier Iriarte, Pablo Valle +3
Autonomous Driving Systems (ADSs) are complex Cyber-Physical Systems (CPSs) that must ensure safety even in uncertain conditions. Modern ADSs often employ Deep Neural Networks (DNN…