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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Artificial Neural Networks for Knowledge Extraction in Spatiotemporal Dynamics and Weather Forecasting

Artificial Neural Networks for Knowledge Extraction in Spatiotemporal Dynamics and Weather Forecasting

Karlbauer, Matthias

Paperback

General Computers

ISBN10: 398944025X
ISBN13: 9783989440258
Publisher: Tubingen Library Publishing
Published: Mar 18 2025
Pages: 190
Weight: 0.69
Height: 0.40 Width: 6.69 Depth: 9.61
Language: German
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.

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