“Design and implementation of machine learning-based anomaly detection in the ITER Tokamak Systems Monitor” - Frontiers in Physics

Mon, 01 Jun 2026 00:00:00 +0000

Authors

Joris Paret
Brian Sammuli
Victor Costa Perez
Nathaniel Saura
Laura Hernandez Cubo
Daniel Iglesias

Abstract

The Tokamak Systems Monitor (TSM) is a software suite under development at ITER that provides operators with an integrated view of the tokamak’s engineering health based on operational instrumentation. A key functionality of TSM is anomaly detection, aimed at identifying unexpected behaviors across a wide range of systems. A dedicated anomaly-detection module integrates multiple machine-learning-based algorithms, ranging from intershot classification of complete pulses to online detection of localized events. The article illustrates the framework with a gyrotron-pulse classifier and a time-localized approach for monitoring magnet power supplies. Automated warnings generated by the module support operators in evaluating anomalies and improving ITER reliability.

Article availability

This open-access paper is published in Frontiers in Physics.

Design and implementation of machine learning-based anomaly detection in the ITER Tokamak Systems Monitor

Mon, 01 Jun 2026 00:00:00 +0000

MLOps | Joris Paret

“Design and implementation of machine learning-based anomaly detection in the ITER Tokamak Systems Monitor” - Frontiers in Physics

Authors

Abstract

Article availability

Design and implementation of machine learning-based anomaly detection in the ITER Tokamak Systems Monitor