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This thesis explores the potential of machine learning methods for improving weather forecasts. Since weather is considered a spatiotemporal process that evolves over space through time, the thesis first investigates the design choices required for machine learning models to simulate synthetic spatiotemporal processes, such as the two-dimensional wave equation. It then develops a method for analyzing machine learning models that enables the extraction of unknown process-relevant context that parameterizes an observed simulated spatiotemporal process of interest. Relating these extracted factors to physical properties leads the thesis to physics-aware machine learning, where it explores how to fuse process knowledge from physics with the learning ability of artificial neural networks. Given the insights from those investigations, a competitive deep learning weather prediction model is designed to understand which design choices support data-driven algorithms to learn a meaningful function that predicts realistic and stable states of the atmosphere over hundreds of hours, days, and weeks into the future.

This thesis explores the potential of machine learning methods for improving weather forecasts. Since weather is considered a spatiotemporal process that evolves over space through time, the thesis first investigates the design choices required for machine learning models to simulate synthetic spatiotemporal processes, such as the two-dimensional wave equation. It then develops a method for analyzing machine learning models that enables the extraction of unknown process-relevant context that parameterizes an observed simulated spatiotemporal process of interest. Relating these extracted factors to physical properties leads the thesis to physics-aware machine learning, where it explores how to fuse process knowledge from physics with the learning ability of artificial neural networks. Given the insights from those investigations, a competitive deep learning weather prediction model is designed to understand which design choices support data-driven algorithms to learn a meaningful function that predicts realistic and stable states of the atmosphere over hundreds of hours, days, and weeks into the future.

Details

Erscheinungsjahr:	2025
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
ISBN-13:	9783989440258
ISBN-10:	398944025X
Sprache:	Englisch Deutsch
Einband:	Kartoniert / Broschiert
Autor:	Karlbauer, Matthias
Hersteller:	Tübingen Library Publishing Eberhard Karls Universität Tübingen
Verantwortliche Person für die EU:	preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Maße:	240 x 170 x 12 mm
Von/Mit:	Matthias Karlbauer
Erscheinungsdatum:	18.03.2025
Gewicht:	0,372 kg

Artikel-ID: 131807933

Details

Erscheinungsjahr:	2025
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
ISBN-13:	9783989440258
ISBN-10:	398944025X
Sprache:	Englisch Deutsch
Einband:	Kartoniert / Broschiert
Autor:	Karlbauer, Matthias
Hersteller:	Tübingen Library Publishing Eberhard Karls Universität Tübingen
Verantwortliche Person für die EU:	preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Maße:	240 x 170 x 12 mm
Von/Mit:	Matthias Karlbauer
Erscheinungsdatum:	18.03.2025
Gewicht:	0,372 kg

Artikel-ID: 131807933

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