# Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics

arXiv: https://arxiv.org/abs/2408.14404 · HTML: https://mlsanity.com/paper/2408.14404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis](https://mlsanity.com/paper/2403.01635/) (2024)

## Built on by (most cited first, 2 of 2)

- [Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2507.09431/) (2025)
- [Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas](https://mlsanity.com/paper/2507.09432/) (2025)

## Abstract (arXiv)

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.