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Time-embedded convolutional neural networks for modeling plasma heat transport

Mufei Luo1,*, Charles Heaton1, Yizhen Wang1, Daniel Plummer1, Mila Fitzgerald1, Francesco Miniati2, Sam M. Vinko1, and Gianluca Gregori1

  • 1Department of Physics, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom
  • 2Mach42, Robert Robinson Avenue, Oxford Science Park, Oxford OX4 4GP, United Kingdom

  • *Contact author: mufei.luo@physics.ox.ac.uk
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Phys. Rev. E 113, 035303 – Published 9 March, 2026

DOI: https://doi.org/10.1103/xx8k-xw91

Abstract

We introduce a time-embedded convolutional neural network (TCNN) for modeling spatiotemporal heat transport in plasmas, particularly under strongly nonlocal conditions. In our earlier work, the Luciani-Mora-Virmont (LMV) Informed Neural Network (LINN) (Luo et al., Phys. Rev. Res. 7, L042017 (2025)) combined prior knowledge from the LMV model with kinetic Particle-in-Cell (PIC) data to improve kernel-based heat-flux predictions. While effective under moderately nonlocal conditions, LINN produced physically inconsistent kernels in strongly time-dependent regimes due to its reliance on the quasistationary LMV formulation. To overcome this limitation, TCNN is designed to capture the coupled evolution of both the normalized heat flux and the characteristic nonlocality parameter using a unified neural architecture informed by underlying physical principles. Trained on fully kinetic PIC simulations, TCNN accurately reproduces nonlocal dynamics across a broad range of collisionalities. Our results demonstrate that the combination of time modulation, coupled prediction, and convolutional depth significantly enhances predictive performance, offering a data-driven yet physically consistent framework for multiscale plasma transport problems.

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Phys. Rev. E 113, 035303– Published 9 March, 2026
  • Received 3 September 2025
  • Accepted 10 February 2026
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