Nuclear fusion | Joris Paret

“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

“Machine Learning-Based Anomaly Detection for ITER's Tokamak Systems Monitor: A Gyrotron Case Study” - IEEE Transactions on Plasma Science

Fri, 08 May 2026 00:00:00 +0000

Authors

Joris Paret
Daniel Iglesias
Daniel Sabio Ruiz
Dilin Meloni
Andrea Antonione
Ruggero Bertazzoni
Giuseppe Carannante
Martino Ferrari
Maria Ortiz de Zuniga
Mario Cavinato
Francisco Sanchez Arcos
Alfredo Portone

Abstract

The Tokamak Systems Monitor (TSM) software will provide ITER operators with timely assessments of machine health, component lifetime, and early warnings of potential faults. Among its key functions, the TSM will employ data-driven anomaly-detection methods to identify unexpected or abnormal behavior across a broad range of systems and diagnostics. This work presents an initial proof of concept for anomaly detection applied to gyrotron pulses, leveraging data from the European gyrotron prototype. The method combines dimensionality reduction and clustering techniques to identify deviations from expected operational patterns. While this first demonstration focuses on intershot anomaly detection for gyrotrons, the methodology is designed to be adaptable to other systems and signals within the TSM.

Article availability

This open-access paper is published in IEEE Transactions on Plasma Science.

Machine Learning-Based Anomaly Detection for ITER's Tokamak Systems Monitor: A Gyrotron Case Study

Fri, 08 May 2026 00:00:00 +0000

“Preliminary machine learning-based calibration strategy for the ITER Tokamak Systems Monitor” - Fusion Engineering and Design

Tue, 21 Oct 2025 00:00:00 +0000

Authors

Joris Paret
Daniel Iglesias
Hyogeon Bak
Matthew Clough
George Vayakis
Michael Walsh

Abstract

A machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM) is presented, focusing on the optimization of dynamic finite-element method (FEM) models essential for monitoring and assessing the structural health of the tokamak. Traditional finite-element model updating techniques are unsuitable for the ITER environment due to constraints such as limited sensor placement and the inability to perform controlled excitations. To overcome these challenges, an automated calibration procedure is proposed, leveraging Bayesian optimization with Gaussian Process regression. The parameter space is efficiently explored by sequentially sampling model configurations, minimizing the discrepancy between simulated and reference modal characteristics. This preliminary work establishes a foundation for calibrating other numerical models within the TSM.

Article availability

This paper is published in Fusion Engineering and Design.

Preliminary machine learning-based calibration strategy for the ITER Tokamak Systems Monitor

Tue, 21 Oct 2025 00:00:00 +0000