TY  - BOOK
AU  - Hellwig, Jan
PY  - 2025
DA  - 2025//
TI  - Efficient solutions to modeling time series of NMR spectroscopic data
PB  - Universität Rostock
CY  - Germany; Rostock
AB  - 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>
UR  - https://purl.uni-rostock.de/rosdok/id00005643
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00005643-5
UR  - https://d-nb.info/1402925719/34
UR  - https://doi.org/10.18453/rosdok_id00005643
DO  - 10.18453/rosdok_id00005643
LA  - English
N1  - vorgelegt von Jan Hellwig
ID  - 1973456362
ER  -