Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA <i>MAT_187_SAMP-1</i> Considering Failure with GISSMO

Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA <i>MAT_187_SAMP-1</i> Considering Failure with GISSMO

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with inte...

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Bibliographic Details
Main Authors: Paul Meißner, Jens Winter, Thomas Vietor
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Materials
Subjects:
parameter identification
machine learning
hyperparameter optimization
LS-DYNA
<i>MAT_187_SAMP-1</i>
GISSMO failure model
Online Access:https://www.mdpi.com/1996-1944/15/2/643

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https://www.mdpi.com/1996-1944/15/2/643

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