Artificial Neural Networks And Machine Learning Icann 2020
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Artificial Neural Networks and Machine Learning – ICANN 2020
The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
Artificial Neural Networks and Machine Learning – ICANN 2020
The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
Artificial Neural Networks for Knowledge Extraction in Spatiotemporal Dynamics and Weather Forecasting
Author: Matthias Karlbauer
language: en
Publisher: BoD – Books on Demand
Release Date: 2025-03-18
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.