Constructing an Surrogate Model for Pedestrian Protection Using Machine Learning

Abstract

In order to shorten the design process of industrial products, an alternative evaluation methodology to CAE is needed. Machine learning methodology is one such method. The authors have applied convolutional neural networks (CNN) to regression problems. Although CNNs are highly capable of interpolating nonlinear physical phenomena, the design of input data should be carefully considered when training on less sensitive data with a small training data set. In this paper, CNNs are adapted to predict crash simulations of a finite element method model that mimics the pedestrian leg protection performance test prescribed by JNCAP.