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Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis

Yingjun Wang1, Zhongyuan Liao1, Shengyu Shi1, *, Zhenpei Wang2, *, Leong Hien Poh3

1 National Engineering Research Center of Novel Equipment for Polymer Processing, the Key Laboratory of Polymer Processing Engineering of the Ministry of Education (South China University of Technology), Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, 510641, China.
2 Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, 138632, Singapore.
3 Department of Civil and Environmental Engineering, National University of Singapore 1 Engineering Drive 2, E1A 07-03, 117576, Singapore.

* Corresponding Authors: Shengyu Shi. Email: ;
   Zhenpei Wang. Email:

(This article belongs to this Special Issue: Recent Developments of Isogeometric Analysis and its Applications in Structural Optimization)

Computer Modeling in Engineering & Sciences 2020, 122(2), 433-458.


Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches.


Cite This Article

Wang, Y., Liao, Z., Shi, S., Wang, Z., Poh, L. H. (2020). Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis. CMES-Computer Modeling in Engineering & Sciences, 122(2), 433–458.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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