Title: Inverse scattering in one-dimensional random media using deep learning
Other Titles: Inverse Streuung in eindimensionalen ungeordneten Medien mittels Deep Learning
Language: English
Authors: Rachbauer, Lukas Michael 
Qualification level: Diploma
Advisor: Rotter, Stefan  
Issue Date: 2019
Number of Pages: 108
Qualification level: Diploma
The inverse scattering problem is in general ill-posed and highly nonlinear. The aim of this thesis is to develop a fast algorithm that provides solutions to such inverse scattering problems in compactly supported one-dimensional random media. A promising candidate for this nonlinear task is Deep Learning, which showed great success in the recent past. The methodology of this approach is to train an Artificial Neural Network for a stochastic class of samples on numerically generated data. Inverse scattering is then performed by means of a simple forward-pass through the Artificial Neural Network. It is shown that in cases where the inverse scattering problem has a unique solution and where the scattering is not too strong, an Artificial Neural Network is able to solve the inverse scattering problem more efficiently than preexisting methods.
Keywords: Streuprobleme; Machine Learning
Scattering problems; Machine Learning
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-125840
Library ID: AC15391313
Organisation: E136 - Institut für Theoretische Physik 
Publication Type: Thesis
Appears in Collections:Thesis

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