Communication Dans Un Congrès Année : 2024

Generative models and simulation to assess uncertainties for tokamak infrared thermography

Résumé

Quantitative infrared thermography, which aims to infer surface temperatures from an infrared image, is challenging in metallic environments. A local increase in the flux measured by the camera may be due to indiscriminately an increase in the temperature of the observed target or due to a change in its surface properties. This paper addresses this ill-posed inverse problem and provides a method to automatically evaluate a confidence interval on the predicted temperature. For this purpose, we use simulated data, generated with ray tracing, to train a deep generative model able to approximate an unknown probability distribution. We apply the proposed method to simulated infrared images of fusion facilities, where infrared thermography is a critical diagnostic for machine protection. We show that the model estimates the temperature with a 6% mean error, and provides a 95% confidence interval, on average 20% of the predicted value, improving the reliability of the measurement.

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Dates et versions

cea-05108308 , version 1 (11-06-2025)

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Alexis Juven, Marie-Hélène Aumeunier, Julien Marot. Generative models and simulation to assess uncertainties for tokamak infrared thermography. MLSP 2024 - IEEE 34th International Workshop on Machine Learning for Signal Processing, Sep 2024, Londres, United Kingdom. ⟨10.1109/MLSP58920.2024.10734728⟩. ⟨cea-05108308⟩
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