Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
This work addresses the need for reliable operation of ITER burning plasmas, but it is incremental as it extends an existing model for sensitivity analysis.
The authors tackled the problem of understanding how physical parameters influence burning plasma behavior in ITER by extending NeuralPlasmaODE to perform a sensitivity analysis of transport and radiation mechanisms, revealing dominant influences of magnetic field strength, safety factor, and impurity content on energy confinement.
Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential equations, to perform a sensitivity analysis of transport and radiation mechanisms in ITER plasmas. Normalized sensitivities of core and edge temperatures and densities are computed with respect to transport diffusivities, electron cyclotron radiation (ECR) parameters, impurity fractions, and ion orbit loss (IOL) timescales. The analysis focuses on perturbations around a trained nominal model for the ITER inductive scenario. Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement, while also revealing how temperature-dependent transport contributes to self-regulating behavior. These findings demonstrate the utility of NeuralPlasmaODE for predictive modeling and scenario optimization in burning plasma environments.