Accueil
| Yann Capdeville (LPG, CNRS, NuTS) |
Introduction |
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| Yann Capdeville (LPG, CNRS, NuTS) |
Hardware and Ressources for scientific HPC (mot de passe: NuTS en minuscules) |
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| Hatim Bourfoune (IDRIS) |
Jean Zay |
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| Aurélien Garivier (ENS Lyon) |
Understanding the Efficiency of Machine Learning: Progress and Challenges |
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| Maëlis Arnould (LGLTPE) |
Diamond open access journals |
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| Marielle Malfante (CEA) |
AI for monitoring: focus on reliability and anomaly detection |
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| David Michéa (EOST) |
Automatic machine learning classification of ground displacement data cubes: application to large InSAR and optical datasets |
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| Sophie Giffard (ISTerre) |
Examples and best practices of using machine learning in geosciences, especially neural convolution networks |
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| Damia Benet (Earth Observatory of Singapore) |
Machine learning for volcanic ash studies |
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| Thomas Bodin (LGLTPE) |
Using Generative Networks for Inverse problems |
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| Nestor Cerpa (Géosciences Montpellier) |
Using deep learning to predict the evolution of mechanical anisotropy in the Earth's mantle due to texture development |
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| Nathanael Schaeffer (ISTerre) |
Neural networks and inverse problem: application to Earth's core flow estimation. |
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| Léonard Seydoux (IPGP) |
AI-based seismic and geodetic data fusion for understanding geophysical phenomena. |
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| Gautier Laurent (ISTO) |
Geocognitive knowledge-based geological modelling : proof of concept
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| Clément Hibert (ITES) |
Contribution of machine learning to environmental seismology |
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| Quentin Blétery (Géoazur) |
Can AI anticipate earthquakes? |
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