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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
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.
citing papers explorer
-
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.
-
Interpreting Temporal Graph Neural Networks with Koopman Theory
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.