Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the grow...

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Bibliographic Details
Main Authors: Paul Meißner, Tom Hoppe, Thomas Vietor
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Applied Sciences
Subjects:
parameter identification
machine learning
convolutional neural networks
Bayesian neural networks
LS-DYNA
MAT_187_SAMP-1
Online Access:https://www.mdpi.com/2076-3417/12/24/12793

Internet

https://www.mdpi.com/2076-3417/12/24/12793

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