  With the increase of the nonlinearity and dimension, it is difficult for the
present popular metamodeling techniques to construct reliable metamodels. To
address this problem, Convolutional Neural Network (CNN) is introduced to
construct a highly accurate metamodel efficiently. Considering the inherent
characteristics of the CNN, it is a potential modeling tool to handle highly
nonlinear and dimensional problems (hundreds-dimensional problems) with the
limited training samples. In order to evaluate the proposed CNN metamodel for
hundreds-dimensional and strong nonlinear problems, CNN is compared with other
metamodeling techniques. Furthermore, several high-dimensional analytical
functions are also employed to test the CNN metamodel. Testing and comparisons
confirm the efficiency and capability of the CNN metamodel for
hundreds-dimensional and strong nonlinear problems. Moreover, the proposed CNN
metamodel is also applied to IsoGeometric Analysis (IGA)-based optimization
successfully.
