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GAC-PINN: Geometry-Adaptive and Constraint-Enhanced Physics-Informed Neural Networks
For systems with steep gradients, sharp interfaces, or severe spatio-temporal coupling, Physics-informed neural networks (PINNs) suffer from spectral bias, geometric inflexibility, and boundary constraint conflicts, which undermine accuracy and convergence. To overcome these issues, we propose a geo
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