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.
DAE-PINN: A physics-informed neural network model for simulating differential-algebraic equations with application to power networks
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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.