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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Bibliografische gegevens
Hoofdauteur:	Botticelli, Massimiliano (auth)
Formaat:	Elektronisch Hoofdstuk
Taal:	Engels
Gepubliceerd in:	KIT Scientific Publishing 2023
Reeks:	Reihe Informationsmanagement im Engineering Karlsruhe 2
Onderwerpen:	Mechanical engineering & materials Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung
Online toegang:	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

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