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-robust 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 inertial confinement fusion plasma.
Fast and accurate prediction of energetic-particle transport driven by Alfv\'en eigenmode (AE) instabilities is essential for integrated modeling workflows used in the design and optimization of burning plasma fusion reactors. In this work, we develop machine-learning-based surrogate models for rapid prediction of ener...
Predictive simulation of turbulent combustion remains challenging because stiff chemical kinetics, turbulent transport, molecular mixing, and heat release interact nonlinearly across unresolved scales. In the filtered or averaged formulations, reaction rate is unclosed, and the integration of detailed chemistry constit...
Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive. Machine-learning surrogates enable fast inference by learning reusable solution operators, yet existing models require separate training f...
Radhika Achikanath Chirakkara, Rajdeep Haldar, Zezheng Song et al.· 0 citations
Accurate system-level prediction of cryogenic liquid storage remains challenging because reduced-order models rely on regime-dependent closures for unresolved heat and mass transfer, particularly under sloshing. We present a physics-integrated neural-network framework that combines a conservation-based zero-dimensional...
P. Marques, Samuel Ahizi, M. A. Mendez· 0 citations
Inertial confinement fusion (ICF) experiments require precise control over high-dimensional capsule-design and pulse-drive parameter spaces to achieve thermonuclear ignition. Sparse experimental diagnostics and partial observations limit the resolution of complex implosion dynamics and make high-precision inverse infer...
Yi-Ming Han, Huan Zhang, Pei-Lin Yao et al.· The Physics of Fluids· 1 citation
The radiative transfer equation (RTE) governs thermal radiation in participating media, which is critical to modeling combustion, atmospheric, high-temperature, and radiative thermal management applications. However, due to the inherently high-dimensional nature of radiative transfer, numerical solutions to the RTE ind...
Daniel Carne· 0 citations
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