SOFT 2024 - Preliminary machine learning-based calibration strategy for the ITER Tokamak Systems Monitor

From September 22 to 27, 2024, I had the opportunity to attend the 33rd Symposium on Fusion Technology (SOFT 2024) at Dublin City University, Ireland. This premier event brought together researchers, engineers, and industry experts from around the world to discuss the latest advancements in fusion energy. The conference covered a wide range of topics, from reactor technology and materials science to plasma diagnostics and control systems, fostering valuable exchanges within the international fusion community.

As part of my research at ITER, I presented a scientific poster on a machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM), a software framework that aggregates data from multiple sensors installed across ITER to assess the machine’s health. This work focuses on improving the accuracy and reliability of numerical models that monitor the ITER tokamak’s structural dynamics. This work explores the use of sequential model-based optimization (SMBO) for the calibration of finite-element models. The goal is to refine structural models of the tokamak using a limited dataset, addressing the challenge of calibrating complex engineering systems under restrictive conditions—a common limitation in fusion experiments due to the harsh operational environment and limited sensor accessibility.

See the poster below or download it as a PDF here.