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

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.

Product details

• Authors: Matthias Karlbauer
• Publisher: Tübingen Library Publishing
• Languages: English, German
• Product format: Paperback / Softback
• Released: 18.03.2025
• EAN: 9783989440258
• ISBN: 978-3-98944-025-8
• No. of pages: 190
• Dimensions: 170 mm x 240 mm x 12 mm
• Weight: 372 g
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT