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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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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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Summary: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, discovering and visualizing connections and correlations in complex phenomena. The extracted knowledge is then validated with domain expertise, revealing potential and limitations of this method.
Physical Description:1 electronic resource (210 p.)
ISBN:KSP/1000158016
Access:Open Access

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