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Home   /   Thesis   /   Anomaly Detection Machine learning Methods for Experimental Plasma Fusion Data Quality - Application to WEST Data

Anomaly Detection Machine learning Methods for Experimental Plasma Fusion Data Quality - Application to WEST Data

Artificial intelligence & Data intelligence Corpuscular physics and outer space Plasma physics and laser-matter interactions Technological challenges


Fusion plasmas in tokamaks have complex non-linear dynamics. In the WEST Tokamak, of the same family as the ITER project, a large amount of heterogeneous experimental fusion data is collected. Ensuring the integrity and quality of this data in real time is essential for the stable and safe operation of the Tokamak. Continuous monitoring and validation are essential, as any disturbance or anomaly can significantly affect our ability to ensure plasma stability, control performance and even lifetime. The detection of unusual patterns or events within the collected data can provide valuable insights and help identify potentially abnormal behavior in plasma operations.
This Ph.D. research aims to study and develop anomaly detection system for WEST -- prefiguring what could be installed on ITER -- by integrate machine learning algorithms, statistical methodologies and signal processing techniques to validate various diagnostic signals in Tokamak operations, including density, interferometry, radiative power and magnetic data.
The expected outcomes are:
– The development of dedicated machine-learning algorithms capable of detecting anomalies in selected time series data from WEST Tokamak.
– The fine-tuning of an operational autonomous system able to ensure data quality in Tokamak reactors, integrated into the WEST AI platform.
– The constitution of a comprehensive database.
– The validation of a data quality framework built for the specific needs of plasma fusion research.


Institut de recherche sur la fusion par confinement magnétique
Service de Physique des Plasmas de Fusion
Groupe Mesures Physiques Plasma
Aix-Marseille Université
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