Fast Solvers of the Lippmann-Schwinger Equation

156 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Accelerating a FFT-based solver for numerical homogenization of periodic media by conjugate gradients (2010, methodology): “Numerical solution of the Lippmann-Schwinger equation is based on a discretization of a unit cell Y into a regular periodic grid with ( 4) is approximated by a trigonometric polynomial e N in the form (cf. [2]) b , N d ), e designates the Fourier coeﬃcients deﬁned in (6) and where N = ( N 1 , . . .”
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks (2017, methodology): “Then, the d-bar equation (6) can be solved using Fast Fourier Transforms using Vainikko’s method [40].”
Numerical solution method for the dbar-equation in the plane (2004, methodology): “Details of this method for the Lippmann–Schwinger equation can be found in [33,38].”
Acoustic inverse scattering using topological derivative of far-field measurements-based L2 cost functionals (2013, unknown): “The discretization method proposed in [41] is used, with the discretization length h adjusted to the wavelength according to h = λ/10 = π/5k.”