Artificial Neural Networks And Machine Learning Icann 2017
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Artificial Neural Networks and Machine Learning – ICANN 2017
The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
Artificial Neural Networks and Machine Learning – ICANN 2017
The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
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.