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

by Matthias Karlbauer
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Current price ₹1,979.00
Original price ₹2,205.00
Original price ₹2,205.00
Original price ₹2,205.00
(-10%)
₹1,979.00
Current price ₹1,979.00

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Book cover type: Paperback
  • ISBN13: 9783989440258
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Tubingen Library Publishing
  • Publisher Imprint: Tubingen Library Publishing
  • Publication Date:
  • Pages: 190
  • Original Price: USD 22.5
  • Language: German
  • Edition: N/A
  • Item Weight: 313 grams
  • BISAC Subject(s): Computer Science

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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