TY - JOUR
T1 - TONR
T2 - An exploration for a novel way combining neural network with topology optimization
AU - Zhang, Zeyu
AU - Li, Yu
AU - Zhou, Weien
AU - Chen, Xiaoqian
AU - Yao, Wen
AU - Zhao, Yong
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - The rapid development of deep learning has opened a new door to the exploration of topology optimization methods. The combination of deep learning and topology optimization has become one of the hottest research fields at the moment. Different from most existing work, this paper conducts an in-depth study on the method of directly using neural networks (NN) to carry out topology optimization. Inspired by the idea from the field of “Inverting Representation of Image” and “Physics-Informed Neural Network”, a topology optimization via neural reparameterization framework (TONR) that can solve various topology optimization problems is formed. The core idea of TONR is Reparameterization, which means the update of the design variables (pseudo-density) in the conventional topology optimization method is transformed into the update of the NN's parameters. The sensitivity analysis in the conventional topology optimization method is realized by automatic differentiation technology. With the update of NN's parameters, the density field is optimized. Some strategies for dealing with design constraints, determining NN's initial parameters, and accelerating training are proposed in the paper. In addition, the solution of the multi-constrained topology optimization problem is also embedded in the TONR framework. Numerical examples show that TONR can stably obtain optimized structures for different optimization problems, including the stress-constrained problem, structural natural frequency optimization problems, compliant mechanism design problems, heat conduction system design problems, and the optimization problem of hyperelastic structures. Compared with the existing methods that combine deep learning with topology optimization, TONR does not need to construct a dataset in advance and does not suffer from structural disconnection. The structures obtained by TONR can be comparable to the conventional methods.
AB - The rapid development of deep learning has opened a new door to the exploration of topology optimization methods. The combination of deep learning and topology optimization has become one of the hottest research fields at the moment. Different from most existing work, this paper conducts an in-depth study on the method of directly using neural networks (NN) to carry out topology optimization. Inspired by the idea from the field of “Inverting Representation of Image” and “Physics-Informed Neural Network”, a topology optimization via neural reparameterization framework (TONR) that can solve various topology optimization problems is formed. The core idea of TONR is Reparameterization, which means the update of the design variables (pseudo-density) in the conventional topology optimization method is transformed into the update of the NN's parameters. The sensitivity analysis in the conventional topology optimization method is realized by automatic differentiation technology. With the update of NN's parameters, the density field is optimized. Some strategies for dealing with design constraints, determining NN's initial parameters, and accelerating training are proposed in the paper. In addition, the solution of the multi-constrained topology optimization problem is also embedded in the TONR framework. Numerical examples show that TONR can stably obtain optimized structures for different optimization problems, including the stress-constrained problem, structural natural frequency optimization problems, compliant mechanism design problems, heat conduction system design problems, and the optimization problem of hyperelastic structures. Compared with the existing methods that combine deep learning with topology optimization, TONR does not need to construct a dataset in advance and does not suffer from structural disconnection. The structures obtained by TONR can be comparable to the conventional methods.
KW - Automatic differentiation
KW - Neural network
KW - Nonlinearity
KW - Reparameterization
KW - Topology optimization
UR - http://www.scopus.com/inward/record.url?scp=85112330329&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2021.114083
DO - 10.1016/j.cma.2021.114083
M3 - 文章
AN - SCOPUS:85112330329
SN - 0045-7825
VL - 386
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
M1 - 114083
ER -