pLaSDI learns a reduced governing equation for time-dependent NLTE atomic kinetics with physics-informed losses enforcing consistency, stability, and steady-state convergence, achieving <2% error on tin charge-state evolution at 5e4-1e5 speedup and stable extrapolation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes DMD and SINDy as new explainability tools for STGNNs, showing they recover interpretable features like infection times and nodes on semi-synthetic data and action-relevant body parts on real motion data.
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Physics-Informed Latent Space Dynamics Identification for Time-Dependent NLTE Atomic Kinetics
pLaSDI learns a reduced governing equation for time-dependent NLTE atomic kinetics with physics-informed losses enforcing consistency, stability, and steady-state convergence, achieving <2% error on tin charge-state evolution at 5e4-1e5 speedup and stable extrapolation.