Document Type
Article
Publication Date
2024
DOI
10.1088/2632-2153/ad9ce7
Publication Title
Machine Learning: Science and Technology
Volume
5
Issue
4
Pages
045070 (1-21)
Abstract
In this work, we propose a data-driven method to discover the latent space and learn the corresponding latent dynamics for a collisional-radiative (CR) model in radiative plasma simulations. The CR model, consisting of high-dimensional stiff ordinary differential equations, must be solved at each grid point in the configuration space, leading to significant computational costs in plasma simulations. Our method employs a physics-assisted autoencoder to extract a low-dimensional latent representation of the original CR system. A flow map neural network is then used to learn the latent dynamics. Once trained, the reduced surrogate model predicts the entire latent dynamics given only the initial condition by iteratively applying the flow map. The radiative power loss (RPL) is then reconstructed using a decoder. Numerical experiments demonstrate that the proposed architecture can accurately predict both the full-order CR dynamics and the RPL rate.
Rights
© 2024 The Authors
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. Any further distribution of this work must maintain attribution to the authors and the title of the work, journal citation and DOI.
Data Availability
Article states: "The data owned by Los Alamos National Laboratory is subject to export control and therefore has certain restrictions. The data that support the findings of this study are available upon reasonable request from the authors."
Original Publication Citation
Xie, X., Tang, Q., & Tang, X. (2024). Latent space dynamics learning for stiff collisional-radiative models. Machine Learning: Science and Technology, 5(4), 1-21, Article 045070. https://doi.org/10.1088/2632-2153/ad9ce7
ORCID
0009-0008-8358-8191 (Xie)
Repository Citation
Xie, Xuping; Tang, Qi; and Tang, Xianzhu, "Latent Space Dynamics Learning for Stiff Collisional-Radiative Models" (2024). Mathematics & Statistics Faculty Publications. 279.
https://digitalcommons.odu.edu/mathstat_fac_pubs/279
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Aerospace Engineering Commons, Artificial Intelligence and Robotics Commons, Astrophysics and Astronomy Commons