計算力学講演会講演論文集
Online ISSN : 2424-2799
セッションID: 16-14
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機械学習を用いた歩行者保護代替モデルの構築
*尾形 海和田 義孝山本 健太郎外処 凌雲
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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.

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