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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| Format: | eBook |
| Language: | English |
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KIT Scientific Publishing
2023
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| Series: | Reihe Informationsmanagement im Engineering Karlsruhe
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| Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
| 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. |
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| Item Description: | Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/ |
| Physical Description: | 1 electronic resource (210 p.) |
| ISBN: | 9783731512950 1000158016 |