Machine learning based data retrieval for inverse scattering problems with incomplete data
Yu Gao, Kai Zhang
Abstract
Abstract We are concerned with the inverse scattering problems associated with incomplete measurement data. It is a challenging topic of increasing importance that arise in many practical applications. Based on a prototypical working model, we propose a machine learning based inverse scattering scheme, which integrates a CNN (convolution neural network) for the data retrieval. The proposed method can effectively cope with the reconstruction under limited-aperture and/or phaseless far-field data. Numerical experiments verify the promising features of our new scheme.
Topics & Concepts
Computer scienceInverse problemInverse scattering problemScheme (mathematics)Convolution (computer science)Convolutional neural networkArtificial intelligenceField (mathematics)Machine learningScatteringArtificial neural networkAlgorithmPattern recognition (psychology)OpticsMathematicsPhysicsPure mathematicsMathematical analysisMicrowave Imaging and Scattering AnalysisNumerical methods in inverse problemsGeophysical Methods and Applications