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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

    Massimiliano Botticelli

    Band 2023,2 von Reihe Informationsmanagement im Engineering Karlsruhe
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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, 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.

    Umfang: XVI, 177 S.

    Preis: 45.00 €

    Fächer:

    Maschinenbau und Verfahrenstechnik 

    Schlagwörter:

    Gasoline Direct Injection Data-Driven Development Machine Learning Application Datengetriebene Entwicklung Anwendung des Maschinellen Lernens Knowledge Discovery Benzin-Direkteinspritzung

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    Botticelli, M. 2023. 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. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000158016
    Botticelli, M., 2023. 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. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000158016
    Botticelli, M. 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. KIT Scientific Publishing, 2023. DOI: https://doi.org/10.5445/KSP/1000158016
    Botticelli, M. (2023). 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. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000158016
    Botticelli, Massimiliano. 2023. 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. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000158016



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    Weitere Informationen

    Veröffentlicht am 3. Juli 2023

    Sprache

    Englisch

    Seitenanzahl:

    210

    ISBN
    Paperback 978-3-7315-1295-0

    DOI
    https://doi.org/10.5445/KSP/1000158016