CrashNet: an encoder–decoder architecture to predict crash test outcomes

Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network...

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Veröffentlicht in:	Data mining and knowledge discovery Jg. 35; H. 4; S. 1688 - 1709
Hauptverfasser:	Belaid, Mohamed Karim, Rabus, Maximilian, Krestel, Ralf
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2021 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Chemistry and Earth Sciences Coders Computer Science Computer simulation Cost reduction Crashworthiness Data Mining and Knowledge Discovery Destructive testing Finite element method Information Storage and Retrieval Machine learning Mathematical models Neural networks Physics Safety management Scalars Special Issue of the Journal Track of ECML PKDD 2021 Statistics for Engineering Time series Traffic accidents Traffic accidents & safety Predictive models Car safety management Time series analysis Supervised deep neural networks
ISSN:	1384-5810, 1573-756X
Online-Zugang:	Volltext
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