An extended Newton implicit layer embedded in a physics-informed DeepONet recovers fast and algebraic states exactly from slow-state predictions for stiff DAEs, achieving low error on high-stiffness examples while satisfying constraints exactly.
A novel discrete-time state-space model for decentralized dynamic state estimation of grid-forming inverters
2 Pith papers cite this work. Polarity classification is still indexing.
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A decentralized DSE method using statistical linearization and matrix-exponential discretization enables stable and accurate state estimation in stiff inverter-dominated power systems at coarse sampling rates.
citing papers explorer
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Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems
An extended Newton implicit layer embedded in a physics-informed DeepONet recovers fast and algebraic states exactly from slow-state predictions for stiff DAEs, achieving low error on high-stiffness examples while satisfying constraints exactly.
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Stiffness-Aware Decentralized Dynamic State Estimation for Inverter-Dominated Power Systems
A decentralized DSE method using statistical linearization and matrix-exponential discretization enables stable and accurate state estimation in stiff inverter-dominated power systems at coarse sampling rates.