# Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis

arXiv: https://arxiv.org/abs/2403.01635 · HTML: https://mlsanity.com/paper/2403.01635/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)

## Built on by (most cited first, 5 of 5)

- [Feedback Favors the Generalization of Neural ODEs](https://mlsanity.com/paper/2410.10253/) (2024)
- [Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics](https://mlsanity.com/paper/2408.14404/) (2024)
- [Discover physical concepts and equations with machine learning](https://mlsanity.com/paper/2412.12161/) (2024)
- [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)

In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential Equations (Neural ODEs) to simulate the intricate energy transfer processes within tokamaks. Our methodology leverages Neural ODEs for the numerical derivation of diffusivity parameters from DIII-D tokamak experimental data, enabling the precise modeling of energy interactions between electrons and ions across various regions, including the core, edge, and scrape-off layer. These regions are conceptualized as distinct nodes, capturing the critical timescales of radiation and transport processes essential for efficient tokamak operation. Validation against DIII-D plasmas under various auxiliary heating conditions demonstrates the model's effectiveness, ultimately shedding light on ways to enhance tokamak performance with deep learning.