Newton iterative shape optimization on the EFIE via BEM maximizes regularized em-chirality for tubular conductors, producing nonintuitive shapes that excite higher-order modes.
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A multi-level neural network framework for inverse scattering that adds frequency-specific levels to recover higher-order Fourier modes of the target while decomposing training into simpler local tasks.
Derives an explicit reconstruction formula for the contrast in the 2D acoustic inverse Born scattering problem by decoupling the linear system into independent triangular subsystems using Zernike decompositions solved via forward substitution.
Extensions of the inverse Born series with Fourier and polarization techniques are proposed to reconstruct scattering potentials from phaseless data.
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Reconstruction methods for inverse scattering problems with phaseless data
Extensions of the inverse Born series with Fourier and polarization techniques are proposed to reconstruct scattering potentials from phaseless data.