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
Abstract: 
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
http://hdl.handle.net/20.500.12708/8585
Library ID: AC15391313
Organisation: E136 - Institut für Theoretische Physik 
Publication Type: Thesis
Hochschulschrift
Appears in Collections:Thesis

Files in this item:


Page view(s)

53
checked on Jul 29, 2021

Download(s)

70
checked on Jul 29, 2021

Google ScholarTM

Check


Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.