Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes

In this work, a novel knowledge discovery framework able to analyze data produced in the Gasoline Direct Injection (GDI) context through machine learning is presented and validated. This approach is able to explore and exploit the investigated design spaces based on a limited number of observations,...

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書誌詳細
第一著者:	Botticelli, Massimiliano (auth)
フォーマット:	電子媒体図書の章
言語:	英語
出版事項:	KIT Scientific Publishing 2023
シリーズ:	Reihe Informationsmanagement im Engineering Karlsruhe 2
主題:	Mechanical engineering & materials Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung
オンライン･アクセス:	OAPEN Library: download the publication OAPEN Library: description of the publication
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes 著者: Botticelli, Massimiliano

出版事項 2023

DOAB: download the publication
DOAB: description of the publication

電子媒体図書の章

Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes

Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes 著者: Botticelli, Massimiliano

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