Using the state of a physically constrained machine learning model trained on the output intensity images to reconstruct the eigenmodes of an optical fiber
Alexander Kabardiadi-Virkovski,
Leander Kläber,
Thomas Schreiber
et al.
Abstract:In this work, the architecture of a machine learning model which is strongly constrained by the physical boundary conditions of the observed optical fibers is presented. The procedure of extraction of the physical relevant information from the trained model is shown. This work aims to estimate the eigenmodes of an optical fiber with the main focus on fibers with few guided modes. We will give an overview of the transit scheme of the expected electromagnetic field properties in a system with low eigenstates by … Show more
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