Discrete Inverse Problems: Insight and Algorithms
By Per Christian Hansen. 2010
1,019 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/f8320f769eb1bcd99ac6021fd2284ebe661fd2fb
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Citations in the corpus
- Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review (2021, background): “Several mathematical challenges often arise in inverse approaches that have historically made it challenging to derive or approximate f−1, and have led to the development of a vast research literature on inverse modeling, including dedicated journals (see the Journal of Inverse and Ill-Posed Problem”
- Two-dimensional permittivity and conductivity imaging by full waveform inversion of multioffset GPR data: a frequency-domain quasi-Newton approach (2014, background): “It is also the effect of Tikhonov regularization (Hansen 2010, p.62).”
- A survey of applications of the MFS to inverse problems (2011, methodology): “Alternatively, instead of the Tikhonov regularization method one can use the truncated singular value decomposition method [34,35].”
- AIR Tools - A MATLAB package of algebraic iterative reconstruction methods (2012, methodology): “In [22] it is discussed that the singular functions are similar to the Fourier basis functions and the discrete Fourier transform (DFT) is therefore used in the NCP method.”
- Non-intrusive low-rank separated approximation of high-dimensional stochastic models (2012, background): “For further information on the selection of this range as well as the details on how GCV is efficiently evaluated in practice, we refer to [19, 72, 37].”
- Estimation of Errors in the Inverse Modeling of Accidental Release of Atmospheric Pollutant: Application to the Reconstruction of the Cesium-137 and Iodine-131 Source Terms from the Fukushima Daiichi Power Plant (2012, methodology): “Several techniques are available and are well described in recent books [Vogel, 2002; Hansen, 2010].”