@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} }