  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.
