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

• প্রধান লেখক: Botticelli, Massimiliano (auth)
• বিন্যাস: বৈদ্যুতিক গ্রন্থের অধ্যায়
• ভাষা: ইংরেজি
• প্রকাশিত: KIT Scientific Publishing 2023
• মালা: Reihe Informationsmanagement im Engineering Karlsruhe 2
• বিষয়গুলি: Mechanical engineering & materials; Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung
• অনলাইন ব্যবহার করুন: OAPEN Library: download the publication; OAPEN Library: description of the publication

সংক্ষিপ্ত

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.

দৈহিক বর্ননা

1 electronic resource (210 p.)

আইসবিএন

KSP/1000158016

প্রবেশাধিকার

Open Access