PowerSINDy identifies nonlinear time-dependent dynamics in real power grid frequency data using sparse regression, with LASSO achieving the lowest stable RMSE of 0.0101 on Continental Europe data.
Sindy-rl: Interpretable and efficient model-based reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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SALSA-RL introduces latent-space stability analysis for actions of pretrained RL agents using encoder-decoder and state-dependent linear dynamics to enable non-invasive interpretability.
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PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency
PowerSINDy identifies nonlinear time-dependent dynamics in real power grid frequency data using sparse regression, with LASSO achieving the lowest stable RMSE of 0.0101 on Continental Europe data.
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SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
SALSA-RL introduces latent-space stability analysis for actions of pretrained RL agents using encoder-decoder and state-dependent linear dynamics to enable non-invasive interpretability.