@Article{cmes.2020.08680,
AUTHOR = {Yingjun Wang, Zhongyuan Liao, Shengyu Shi, Zhenpei Wang, Leong Hien Poh},
TITLE = {Data-Driven Structural Design Optimization for Petal-Shaped  Auxetics Using Isogeometric Analysis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {2},
PAGES = {433--458},
URL = {http://www.techscience.com/CMES/v122n2/38328},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2020.08680}
}