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.
Physics-informed neural networks with hard constraints for inverse design,
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Trefftz-PINNs preserve the global topology of magnetic field lines and velocity streamlines more reliably than standard PINNs even when mean squared errors are matched.
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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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.
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Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation
Trefftz-PINNs preserve the global topology of magnetic field lines and velocity streamlines more reliably than standard PINNs even when mean squared errors are matched.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.