CC-PINN introduces a cross-correlated residual and Fourier-feature MLP to achieve more robust high-contrast reconstructions and faster convergence than standard PINNs in inverse scattering.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Beyond Data-Physics Consistency: A Cross-Correlated Physics-Informed Neural Network for Robust Inverse Scattering
CC-PINN introduces a cross-correlated residual and Fourier-feature MLP to achieve more robust high-contrast reconstructions and faster convergence than standard PINNs in inverse scattering.