Guillaume Coulaud
Doctorant - Détection d'anomalies dans les données climatiques massives
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Libre accès
7
Documents
Affiliations actuelles
- Université de Montpellier (UM)
- Sciences environnementales guidées par les données (IROKO)
Identifiants chercheurs
Compétences
Time series
Anomaly detection
Deep learning
Data science
Publications
Publications
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ClimBurst: A Novel Method to Detect Climatological Anomalies Over Time and SpaceGeophysical Research Letters, 2025, 52 (19), pp.e2025GL117095. ⟨10.1029/2025gl117095⟩ |
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ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and SpaceCIKM 2025 - 34th ACM International Conference on Information and Knowledge Management, ACM, Nov 2025, Seoul, South Korea. pp.6629-6633, ⟨10.1145/3746252.3761466⟩ |
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Investigations on Physics-Informed Neural Networks for Aerodynamics58th 3AF International Conference on Applied Aerodynamics, Mar 2024, Orleans, France, France |
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Unsupervised data-driven detection of exceptional atmospheric trajectories2026 |
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TRAKNN: Efficient Trajectory Aware Spatiotemporal kNN for Rare Meteorological Trajectory Detection2026 |
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Leveraging Data Seasonality and Matrix Profile for Anomaly Detection: Application to Climate Time SeriesRR-9572, Inria. 2025 |
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Physics-Informed Neural Networks for Multiphysics Coupling: Application to Conjugate Heat TransferRR-9520, Université Côte d'Azur, Inria, CNRS, LJAD. 2023 |
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