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    <title>Efficient solutions to modeling time series of NMR spectroscopic data</title>
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    <namePart>Hellwig, Jan</namePart>
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    <namePart>Neymeyr, Klaus</namePart>
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    <namePart>Kazimierczuk, Krzysztof</namePart>
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  <abstract type="Summary">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.&lt;eng&gt;</abstract>
  <abstract type="Summary">In dieser Arbeit wird zunächst die Frage behandelt, ob und unter welchen Voraussetzungen die Lösung dieses Modellierungsproblems eindeutig ist. Da angenommen werden kann, dass sich die Spektren in Zeitrichtung stetig und glatt verhalten, interpolieren wir die Parameter zur effizienten Lösung des Optimierungsproblems. Diese Methode kann des Weiteren durch die Anwendung von neuronalen Netzen verbessert werden, die die Initialisierung der Optimierung verbessern. Wir wenden die Algorithmen sowohl auf künstlich erzeugten als auch auf experimentellen Datensätzen an.&lt;ger&gt;</abstract>
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  <note type="statement of responsibility">vorgelegt von Jan Hellwig</note>
  <note>GutachterInnen: Klaus Neymeyr (Universität Rostock) ; Krzysztof Kazimierczuk (Universität Warschau)</note>
  <note type="thesis">Dissertation Universität Rostock 2025</note>
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      <title>Efficient solutions to modeling time series of NMR spectroscopic data</title>
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      <namePart>Hellwig, Jan, 1999 - </namePart>
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