<div class="csl-bib-body">
<div class="csl-entry">Rachbauer, L. M. (2019). <i>Inverse scattering in one-dimensional random media using deep learning</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.64845</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.64845
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/8585
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.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.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Streuprobleme
de
dc.subject
Machine Learning
de
dc.subject
Scattering problems
en
dc.subject
Machine Learning
en
dc.title
Inverse scattering in one-dimensional random media using deep learning
en
dc.title.alternative
Inverse Streuung in eindimensionalen ungeordneten Medien mittels Deep Learning