Publicacions en col·laboració amb investigadors/es de Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (129)

2024

  1. A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components

    Sensors, Vol. 24, Núm. 13

  2. Advancing MARFE detection in JET's operational camera videos through Machine Learning techniques

    Fusion Engineering and Design, Vol. 205

  3. Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic

    Sensors, Vol. 24, Núm. 9

  4. Discovery of a dormant 33 solar-mass black hole in pre-release Gaia astrometry

    Astronomy and Astrophysics, Vol. 686

  5. Overview of T and D-T results in JET with ITER-like wall

    Nuclear Fusion, Vol. 64, Núm. 11

  6. Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

    Nuclear Fusion, Vol. 64, Núm. 4

2023

  1. A programmable web platform for distributed access, analysis, and visualization of data

    Fusion Engineering and Design, Vol. 197

2022

  1. Disruption prediction with artificial intelligence techniques in tokamak plasmas

    Nature Physics, Vol. 18, Núm. 7, pp. 741-750

  2. Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

    Nature Physics, Vol. 18, Núm. 7, pp. 776-782

  3. Frontiers in data analysis methods: From causality detection to data driven experimental design

    Plasma Physics and Controlled Fusion, Vol. 64, Núm. 2

  4. Overview of JET results for optimising ITER operation

    Nuclear Fusion, Vol. 62, Núm. 4

  5. Performance Analysis of the Centroid Method Predictor Implemented in the JET Real Time Network

    Plasma Physics and Controlled Fusion, Vol. 64, Núm. 11

2021

  1. Nuclear Fusion Pattern Recognition by Ensemble Learning

    Complexity, Vol. 2021

2020

  1. A linear equation based on signal increments to predict disruptive behaviours and the time to disruption on jet

    Nuclear Fusion, Vol. 60, Núm. 2

  2. Automatic Recognition of Anomalous Patterns in Discharges by Applying Deep Learning

    Fusion Science and Technology, Vol. 76, Núm. 8, pp. 925-932

  3. Automatic recognition of anomalous patterns in discharges by recurrent neural networks

    Fusion Engineering and Design, Vol. 154

  4. Automatic recognition of plasma relevant events: Implications for ITER

    Fusion Engineering and Design, Vol. 156

  5. Global scaling of the heat transport in fusion plasmas

    Physical Review Research, Vol. 2, Núm. 1

2019

  1. Assessment of linear disruption predictors using JT-60U data

    Fusion Engineering and Design, Vol. 146, pp. 1291-1294

  2. Full-orbit and drift calculations of fusion product losses due to explosive fishbones on JET

    Nuclear Fusion, Vol. 59, Núm. 1