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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Bibliographic Details
Main Author:	Botticelli, Massimiliano (auth)
Format:	Electronic Book Chapter
Language:	English
Published:	KIT Scientific Publishing 2023
Series:	Reihe Informationsmanagement im Engineering Karlsruhe 2
Subjects:	Mechanical engineering & materials Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung
Online Access:	OAPEN Library: download the publication OAPEN Library: description of the publication
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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 by Botticelli, Massimiliano

Published 2023

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DOAB: description of the publication

Electronic Book Chapter

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 by Botticelli, Massimiliano

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