Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations

TL;DR

This paper introduces a neural ODE-based inverse modeling method to optimize external sources for controlled plasma behavior in tokamaks, enabling precise regulation of plasma parameters for fusion energy.

Contribution

It presents a novel neural ODE framework for inverse modeling of plasma control, integrating physical mechanisms and enabling efficient computation of external source profiles.

Findings

Successfully computes external source profiles for desired plasma trajectories.

Transforms plasma simulation into a control-oriented model.

Applicable to current and future fusion devices.

Abstract

Achieving controlled burning plasma in tokamaks requires precise regulation of external particle and energy sources to reach and maintain target core densities and temperatures. This work presents an inverse modeling approach using a multinodal plasma dynamics model based on neural ordinary differential equations (Neural ODEs). Given a desired time evolution of nodal quantities such as deuteron density or electron temperature, we compute the external source profiles, such as neutral beam injection (NBI) power, that drive the plasma toward the specified behavior. The approach is implemented within the NeuralPlasmaODE framework, which models multi-region, multi-timescale transport and incorporates physical mechanisms including radiation, auxiliary heating, and internodal energy exchange. By formulating the control task as an optimization problem, we use automatic differentiation through the Neural ODE solver to minimize the discrepancy between simulated and target trajectories. This framework transforms the forward simulation tool into a control-oriented model and provides a practical method for computing external source profiles in both current and future fusion devices.