%0 Thesis %1 praditia2023physicsinformed %A Praditia, Timothy %B Mitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart %C Stuttgart %D 2023 %I Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung %K %N 300 %R 10.18419/opus-13229 %T Physics-informed neural networks for learning dynamic, distributed and uncertain systems %@ 978-3-910293-04-5

@phdthesis{praditia2023physicsinformed, added-at = {2023-07-06T13:30:44.000+0200}, address = {Stuttgart}, author = {Praditia, Timothy}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2561579d33cdf49675c49671c030d1871/unibiblio-3}, doi = {10.18419/opus-13229}, eventdate = {2023-05-08}, interhash = {740942a74b1bd4d8c5434ef385732eed}, intrahash = {561579d33cdf49675c49671c030d1871}, isbn = {978-3-910293-04-5}, keywords = {}, language = {eng}, number = 300, publisher = {Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung}, school = {Universität Stuttgart}, series = {Mitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart}, supervisor = {Nowak, Wolfgang}, supervisorgnd = {1045721050}, timestamp = {2023-07-06T13:31:42.000+0200}, title = {Physics-informed neural networks for learning dynamic, distributed and uncertain systems}, type = {Dissertation}, year = 2023 }