# Physics-Informed Latent Space Dynamics Identification for Time-Dependent NLTE Atomic Kinetics

Source: [Physics-Informed Latent Space Dynamics Identification for Time-Dependent NLTE Atomic Kinetics](<https://www.sota2.com/research/paper/69e704647d0172140ec7eb26>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-22T05:21:28.943Z

Authors: Jeongwoo Nam, William Anderson, Youngsoo Choi, Hai P. Le, Mark E. Foord, Byoung Ick Cho, Haewon Jeong, Min Sang Cho

Published: 2026-04-17T19:47:14.000Z

## Abstract

Non-local thermodynamic equilibrium (NLTE) calculations remain a major computational bottleneck in radiation--hydrodynamics, while most existing machine-learning surrogates treat NLTE as a static input--output mapping rather than a kinetic evolution problem. Here, we present a physics-informed Latent Space Dynamics Identification (pLaSDI) framework specifically designed for NLTE atomic kinetics, which captures the time-dependent atomic kinetics of non-equilibrium plasmas through an explicit reduced governing equation. To ensure the physical reliability of the reduced model, we impose physics-informed loss terms that enforce macroscopic consistency, dynamical stability, and convergence to the correct steady state during long-time integration. Applied to tin NLTE population data generated along hydrodynamically modeled temperature--density trajectories relevant to extreme ultraviolet (EUV) lithography plasmas, the model accurately reproduces charge-state evolution and mean charge state with errors below 2\\%, achieves speedups of approximately $5\\times10^{4}$--$10^{5}$, and remains stable outside the training trajectories by converging toward physically admissible states and the correct steady-state solution under fixed plasma conditions. These results show that careful physics-informed design of the latent dynamics, rather than data fitting alone, is essential for constructing fast, stable, and physically reliable extrapolative surrogates for time-dependent NLTE kinetics.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.16664>)

[PDF](<https://arxiv.org/pdf/2604.16664.pdf>)