@Book{1973456362,
author="Hellwig, Jan",
title="Efficient solutions to modeling time series of NMR spectroscopic data",
year="2025",
address="Germany; Rostock",
abstract="In this work, we first discuss the question of whether the problem of modeling NMR time series has ambiguous or unique solutions in the continuous and discrete case and using several common types of model functions. In the second part, we propose an efficient and automated solution, by interpolating the parameters using cubic spline functions. Additionally, we improve on the optimization approach by introducing convolutional neural networks, which better initialize the optimization routines. We demonstrate the functionality of our algorithms on constructed and experimental data sets.<eng>",
school="Universit{\"a}t Rostock",
note="vorgelegt von Jan Hellwig",
note="GutachterInnen: Klaus Neymeyr (Universit{\"a}t Rostock) ; Krzysztof Kazimierczuk (Universit{\"a}t Warschau)",
note="Dissertation Universit{\"a}t Rostock 2025",
doi="10.18453/rosdok_id00005643",
url="https://purl.uni-rostock.de/rosdok/id00005643",
url="https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00005643-5",
url="https://d-nb.info/1402925719/34",
url="https://doi.org/10.18453/rosdok_id00005643",
language="English"
}