TY - JOUR
T1 - Neural network-based surrogate model for a bifurcating structural fracture response
AU - van de Weg, Bram Pieter
AU - Greve, Lars
AU - Andres, Michael
AU - Eller, Tom
AU - Rosic, Bojana
N1 - Elsevier deal
PY - 2021/1
Y1 - 2021/1
N2 - A finite element model of a tapered tensile specimen with a hardness transition zone in the gauge section and a varying width parameter is used for creating corresponding solution snapshots. Subsequently, a long short-term memory (LSTM) recurrent neural network (RNN) is trained on the selected snapshots, providing a parametrized solution model for a computationally efficient prediction of the structural response, allowing real-time model evaluation. In addition to a parametrized solution of the fracture localization, the model also captures the bifurcating local mesh deformation. The internal solution strategy of the RNN for predicting the bifurcation phenomenon is investigated and visualized.
AB - A finite element model of a tapered tensile specimen with a hardness transition zone in the gauge section and a varying width parameter is used for creating corresponding solution snapshots. Subsequently, a long short-term memory (LSTM) recurrent neural network (RNN) is trained on the selected snapshots, providing a parametrized solution model for a computationally efficient prediction of the structural response, allowing real-time model evaluation. In addition to a parametrized solution of the fracture localization, the model also captures the bifurcating local mesh deformation. The internal solution strategy of the RNN for predicting the bifurcation phenomenon is investigated and visualized.
KW - UT-Hybrid-D
U2 - 10.1016/j.engfracmech.2020.107424
DO - 10.1016/j.engfracmech.2020.107424
M3 - Article
SN - 0013-7944
VL - 241
JO - Engineering fracture mechanics
JF - Engineering fracture mechanics
M1 - 107424
ER -