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Titlebook: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Informat; Fabian Guignard Book 2022 The Editor

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發(fā)表于 2025-3-21 19:20:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Informat
編輯Fabian Guignard
視頻videohttp://file.papertrans.cn/702/701077/701077.mp4
概述Follows a comprehensive end-to-end data analysis workflow.Covers concrete applications in environmental modelling and renewable energy assessement.Benefits environmental analysts and researchers worki
叢書(shū)名稱Springer Theses
圖書(shū)封面Titlebook: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Informat;  Fabian Guignard Book 2022 The Editor
描述.The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energ
出版日期Book 2022
關(guān)鍵詞Machine Learning; Deep Learning; Uncertainty Quantification; Model Variance; Artificial Neural Network; S
版次1
doihttps://doi.org/10.1007/978-3-030-95231-0
isbn_softcover978-3-030-95233-4
isbn_ebook978-3-030-95231-0Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Fabian Guignarda’s art and the work of other modern twentieth-century artists, considering its links to Surrealism, Pop Art and the Mexican Muralism Movement. Parra exhibited in open-air art fairs, museums and cultural centre978-3-030-38409-8978-3-030-38407-4
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is created by using a three-dimensional (3D)-CT volume analyzer Synapse VINCENT (Fujifilm Co., Tokyo, Japan), which makes surgical simulation of sublobar resection based on bronchial or vascular structures, and the appropriate area of segmentectomy is decided based on surgical margin. Under general
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Fabian Guignardtheir accuracy remains variable and somewhat subjective. More recently, fluorescent imaging with indocyanine green (ICG) has shown to objectively identify the potentially ischemic bowel tissue, thereby reducing the rates of failure of anastomosis. Quantitative assessment of the fluorescent perfusion
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Fabian Guignardtabilities in the displacement field disappear for three-dimensional examples at large strains. In addition, previously unknown limitations of the enhanced peridynamic correspondence formulation are shown within the numerical examples. These are slight, non-physical, deviations in the deformation fi
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Fabian Guignardtabilities in the displacement field disappear for three-dimensional examples at large strains. In addition, previously unknown limitations of the enhanced peridynamic correspondence formulation are shown within the numerical examples. These are slight, non-physical, deviations in the deformation fi
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