A pressure-Poisson objective combined with equality-constrained neural networks and adaptive viscosity enables unsupervised simulation of high-Reynolds-number incompressible flows including spontaneous vortex shedding.
Conditionally adaptive augmented la- grangian method for physics-informed learning of forward and inverse problems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
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
-
Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks
A pressure-Poisson objective combined with equality-constrained neural networks and adaptive viscosity enables unsupervised simulation of high-Reynolds-number incompressible flows including spontaneous vortex shedding.
-
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.