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This content will become publicly available on September 1, 2024

Title: A publicly available PyTorch-ABAQUS UMAT deep-learning framework for level-set plasticity
This paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. In particular, our focus is to introduce a practical framework that can be deployed for engineering analysis that employs a user-defined material subroutine (UMAT/VUMAT) for ABAQUS, which is written in FORTRAN. To accomplish this task while leveraging the back-propagation learning algorithm to speed up the neural-network training, we introduce an interface code where the weights and biases of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic FORTRAN code that can be a part of the UMAT/VUMAT algorithm. To enable third-party validation, we purposely make all the data sets, source code used to train the neural-network-based constitutive models, and the trained models available in a public repository. Furthermore, the practicality of the workflow is then further tested on a dataset for anisotropic yield function to showcase the extensibility of the proposed framework. A number of representative numerical experiments are used to examine the accuracy, robustness and reproducibility of the results generated by the neural network models.  more » « less
Award ID(s):
1846875
NSF-PAR ID:
10487111
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ScienceDirect
Date Published:
Journal Name:
Mechanics of Materials
Volume:
184
Issue:
C
ISSN:
0167-6636
Page Range / eLocation ID:
104682
Subject(s) / Keyword(s):
["Public availability","Reproducibility","Neural-network-based constitutive models","ABAQUS subroutine"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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