Application of machine learning procedures for mechanical system modelling: capabilities and caveats to prediction-accuracy

Application of machine learning procedures for mechanical system modelling: capabilities and caveats to prediction-accuracy

Abstract This article presents an investigation about prediction accuracy of multi-parametric models derived from numerical data. Three different mechanical test-cases are used for the generation of the numerical data. From this data, models are derived for the prediction of characteristic variation...

Full description

Bibliographic Details
Main Authors: Thomas Groensfelder, Fabian Giebeler, Marco Geupel, David Schneider, Rebecca Jaeger
Format: Article
Language:English
Published: SpringerOpen 2020-06-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
System modelling
Machine Learning
Numerical experiment
Parametric Finite Element Analysis
Prediction accuracy
Sensitivity study
Online Access:http://link.springer.com/article/10.1186/s40323-020-00163-4

Internet

http://link.springer.com/article/10.1186/s40323-020-00163-4

Similar Items