Machine Learning

Description of the modular machine-learning anomaly-detection framework developed for ITER’s Tokamak Systems Monitor.

The Tokamak Systems Monitor (TSM) is a software suite under development at ITER that provides operators with an integrated view of the …

Proof of concept for detecting anomalous gyrotron pulses in ITER’s Tokamak Systems Monitor using dimensionality reduction and clustering.

The ITER Tokamak Systems Monitor will provide operators with timely assessments of machine health, component lifetime, and early …

A machine-learning-based calibration strategy for the ITER Tokamak Systems Monitor, using Bayesian optimization and Gaussian-process regression to efficiently update finite-element models.

A machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM) is presented, focusing on the optimization of …

Participation to the 31st IEEE Symposium on Fusion Engineering (SOFE 2025) in Boston, presenting a scientific poster on machine learning-based anomaly detection for the ITER Tokamak Systems Monitor (TSM), using gyrotron pulse data from the European industrial prototype for ITER.

Participation to the 33rd Symposium on Fusion Technology (SOFT 2024) in Dublin, presenting a scientific poster on a machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM) software suite.

This study focuses on the use of dimensionality reduction techniques to assess the structural heterogeneity of glassy binary mixtures. This work is in collaboration with Daniele Coslovich and Robert L. Jack.

partycls is a Python package for the analysis of systems of interacting particles using unsupervised machine learning methods. A new major release (2.0.0) was just published.