{ "item_title" : "Artificial Neural Networks for Knowledge Extraction in Spatiotemporal Dynamics and Weather Forecasting", "item_author" : [" Matthias Karlbauer "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/98/944/025/398944025X_b.jpg", "price_data" : { "retail_price" : "22.50", "online_price" : "22.50", "our_price" : "22.50", "club_price" : "22.50", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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