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

• Glavni autor: Botticelli, Massimiliano (auth)
• Format: Elektronički Poglavlje knjige
• Jezik: engleski
• Izdano: KIT Scientific Publishing 2023
• Serija: Reihe Informationsmanagement im Engineering Karlsruhe 2
• Teme: Mechanical engineering & materials; Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung
• Online pristup: OAPEN Library: download the publication; OAPEN Library: description of the publication

Sažetak

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.

Opis fizičkog objekta

1 electronic resource (210 p.)

ISBN

KSP/1000158016

Pristup

Open Access