Time-Embedded Convolutional Neural Networks for Modeling Plasma Heat Transport
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918137298944000 |
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| author | Luo, Mufei Heaton, Charles Wang, Yizhen Plummer, Daniel Fitzgerald, Mila Miniati, Francesco Vinko, Sam M. Gregori, Gianluca |
| author_facet | Luo, Mufei Heaton, Charles Wang, Yizhen Plummer, Daniel Fitzgerald, Mila Miniati, Francesco Vinko, Sam M. Gregori, Gianluca |
| contents | 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 LMV-Informed Neural Network (LINN) (Luo et al., arXiv:2506.16619) 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 quasi-stationary 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Embedded Convolutional Neural Networks for Modeling Plasma Heat Transport Luo, Mufei Heaton, Charles Wang, Yizhen Plummer, Daniel Fitzgerald, Mila Miniati, Francesco Vinko, Sam M. Gregori, Gianluca Plasma Physics 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 LMV-Informed Neural Network (LINN) (Luo et al., arXiv:2506.16619) 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 quasi-stationary 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. |
| title | Time-Embedded Convolutional Neural Networks for Modeling Plasma Heat Transport |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2509.06088 |