An adaptive KAN-based PINN framework for axisymmetric pulsar magnetosphere achieves O(1e-6) PDE residual errors, under-20-minute convergence, smaller stellar radii, and a correction to the flux-T-point equation.
Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity
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Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.
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An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks
An adaptive KAN-based PINN framework for axisymmetric pulsar magnetosphere achieves O(1e-6) PDE residual errors, under-20-minute convergence, smaller stellar radii, and a correction to the flux-T-point equation.
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A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.