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,...
Saved in:
Main Author: | Botticelli, Massimiliano (auth) |
---|---|
Format: | Electronic Book Chapter |
Language: | English |
Published: |
KIT Scientific Publishing
2023
|
Series: | Reihe Informationsmanagement im Engineering Karlsruhe
|
Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
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
by: Botticelli, Massimiliano
Published: (2023) -
Kalibrierung von Magnet-Injektoren für Benzin-Direkteinspritzsysteme mittels Körperschall
by: Christ, Konrad
Published: (2011) -
Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
by: Gajek, Sebastian
Published: (2023) -
Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
by: Gajek, Sebastian
Published: (2023) -
Internal Combustion Engine Technology and Applications of Biodiesel Fuel
Published: (2021)