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.
Physics-informed neural networks with trust-region sequential quadratic programming
2 Pith papers cite this work. Polarity classification is still indexing.
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AdamFLIP treats PDE constraint residuals in PINNs as a controlled dynamical system, computes Lagrange multipliers via feedback linearization to drive residuals to zero, and applies Adam-style adaptation to the resulting gradient for scalable hard-constrained training.
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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AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
AdamFLIP treats PDE constraint residuals in PINNs as a controlled dynamical system, computes Lagrange multipliers via feedback linearization to drive residuals to zero, and applies Adam-style adaptation to the resulting gradient for scalable hard-constrained training.