設計工学・システム部門講演会講演論文集
Online ISSN : 2424-3078
セッションID: 2209
会議情報

深層学習を用いた車両運動性能の代理モデルの開発
*牧野 晃平三輪 誠新谷 浩平阿部 充治佐々木 裕
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Nowadays, Computer Aided Engineering (CAE) is indispensable for vehicle design; however, its computation time is a heavy burden when tuning design parameters. In this respect, some studies in several fields have been carried out for replacing CAE with machine learning methods. In this paper, we propose neural sequence modeling with Recurrent Neural Networks (RNNs), which are neural networks that treat sequences such as temporal sequences to imitate CAE as a computationally-efficient neural surrogate model. Our target task is the NCAP Fishhook test to evaluate vehicle dynamics of the rollover propensity. Our model reduced the error in approximately 10% for NCAP Fishhook test dataset compared to that of the baseline hierarchical neural network model. Furthermore, our model has task-specific characteristics: (1) skip connection, (2) hybrid loss and (3) scheduled sampling to improve performance and stability. We confirmed that the skip connection reduced errors in the additional ablation study. Our experiments showed that the sequential model is effective as a surrogate model for CAE.

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