[Published online Journal of Computer Chemistry, Japan Vol.18, 132-135, by J-STAGE]
<Title:> 転移学習と生成ネットワークの試行事例
<Author(s):> 伊藤 雅仁, 篠嶋 友也, 望月 祐志, 秋永 宜伸, 小杉 範仁
<Corresponding author E-Mill:> fullmoon(at)rikkyo.ac.jp
<Abstract:> Transfer learning has attracted interests because of the reduction of training costs. We applied this technique to the analyses for visualized results of computational fluid dynamics (CFD) simulations on 2-dimensional wing models. The accuracy and cost reduction were addressed. Preliminary studies of generative network have been made as well.
<Keywords:> Transfer learning, Generative network, Keras, 2-dimensional wing model, Computational fluid dynamics
<URL:> https://www.jstage.jst.go.jp/article/jccj/18/3/18_2019-0023/_article/-char/ja/
