Resolution-Independent Machine Learning Heat Flux Closure for ICF Plasmas

TL;DR

This paper introduces a resolution-independent machine learning heat flux closure for ICF plasmas, capable of accurate predictions across different resolutions and improving the integration of data-driven models into plasma simulations.

Contribution

The authors develop a Fourier Neural Operator-based heat flux closure that remains predictive across resolutions and demonstrates effective generalization in plasma modeling.

Findings

Models trained on coarse data accurately predict heat flux at finer resolutions.

The learned closure reproduces temperature evolution faithfully in simulations.

The approach enhances the practicality of embedding ML closures into PDE solvers.

Abstract

Accurate modeling of heat flux in inertial confinement fusion plasmas requires closures that remain predictive far from local equilibrium and across disparate spatial and temporal resolutions. We develop a resolution-independent machine-learning heat flux closure trained on particle-in-cell simulations using a Fourier Neural Operator. Two nonlocal electron thermal conduction models are trained and tested. When embedded self-consistently into the electron energy equation, the learned closure faithfully reproduces the temperature evolution and shows good temporal extrapolation and generalization capability. Remarkably, models trained on coarse-resolution data accurately predict heat flux when deployed in substantially finer-resolution implicit, iterative solvers of the energy equation, significantly enhancing the practicality of embedding data-driven closures into partial differential equation solvers. These results establish a data-driven closure that bridges kinetic and fluid descriptions and provides a viable pathway for treating machine learning as an iterative solver within the radiation-hydrodynamic simulations of ICF plasma.