Corpus ID: 225103451

Machine Learning Regularized Solution of the Lippmann-Schwinger Equation

@article{Pang2020MachineLR,
  title={Machine Learning Regularized Solution of the Lippmann-Schwinger Equation},
  author={Subeen Pang and G. Barbastathis},
  journal={arXiv: Computational Physics},
  year={2020}
}
Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regularize this inevitably ill-conditioned problem, we propose a machine learning approach: a recurrent neural network with long short-term memory (LSTM) and with the null space projection of the Lippmann-Schwinger kernel on the recurrence path. The learning method is trained using examples of typical scattering potentials… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 27 REFERENCES
Partial Differential Equations - Analytical and Numerical Methods
Proceedings of the National Academy of Sciences, USA
A wavelet tour of signal processing
IEEE transactions on image processing 13
  • 600
  • 2004
and T
  • Brox, in International Conference on Medical image computing and computer-assisted intervention
  • 2015
Light: Science & Applications 9
  • 1
  • 2020
and G
  • Barbastathis, arXiv preprint arXiv:2007.10734
  • 2020
IEEE journal of biomedical and health informatics 24
  • 568
  • 2019
...
1
2
3
...