Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics

TL;DR

This paper introduces NeuralPlasmaODE, a neural ODE-based model for simulating ITER burning plasma dynamics, capturing energy transfer processes and preventing thermal runaway through transfer learning from experimental data.

Contribution

It presents a novel neural ODE model for multi-region plasma transport, incorporating transfer learning to improve simulation accuracy for fusion plasma dynamics.

Findings

Neural ODEs accurately model plasma energy transfer processes.

Transfer learning from DIII-D data enhances simulation efficiency.

Model demonstrates effective heat removal in ITER scenarios.

Abstract

The dynamics of burning plasmas in tokamaks are crucial for advancing controlled thermonuclear fusion. This study applies the NeuralPlasmaODE, a multi-region multi-timescale transport model, to simulate the complex energy transfer processes in ITER deuterium-tritium (D-T) plasmas. Our model captures the interactions between energetic alpha particles, electrons, and ions, which are vital for understanding phenomena such as thermal runaway instability. We employ neural ordinary differential equations (Neural ODEs) for the numerical derivation of diffusivity parameters, enabling precise modeling of energy interactions between different plasma regions. By leveraging transfer learning, we utilize model parameters derived from DIII-D experimental data, enhancing the efficiency and accuracy of our simulations without training from scratch. Applying this model to ITER's inductive and non-inductive operational scenarios, our results demonstrate that radiation and transport processes effectively remove excess heat from the core plasma, preventing thermal runaway instability. This study underscores the potential of machine learning in advancing our understanding and control of burning plasma dynamics in fusion reactors.