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An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
Computer Methods in Applied Mechanics and Engineering ( IF 6.9 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.cma.2020.113375
M. Cheng , F. Fang , C.C. Pain , I.M. Navon

Abstract Considering the high computation cost required in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in years, aiming on reducing CPU time. In this work, we propose a hybrid deep adversarial autoencoder (VAE-GAN) to integrate generative adversarial network (GAN) and variational autoencoder (VAE) for predicting parameterized nonlinear fluid flows in spatial and temporal dimensions. High-dimensional inputs are compressed into the low-dimensional representations by nonlinear functions in a convolutional encoder. In this way, the predictive fluid flows reconstructed in a convolutional decoder contain the dynamic fluid flow physics of high nonlinearity and chaotic nature. In addition, the low-dimensional representations are applied to the adversarial network for model training and parameter optimization, which enables fast computation process. The capability of the hybrid VAE-GAN is illustrated by varying inputs on a flow past a cylinder test case as well as a second case of water column collapse. Numerical results show that this hybrid VAE-GAN has successfully captured the spatio-temporal flow features with CPU speed-up of three orders of magnitude. These promising results suggest that the hybrid VAE-GAN can play a critical role in efficiently and accurately predicting complex flows in future research efforts.

中文翻译:

一种用于参数化非线性流体流动建模的高级混合深度对抗自编码器

摘要 考虑到传统计算流体动力学模拟所需的高计算成本,近年来,机器学习方法被引入到流体动力学模拟中,旨在减少CPU时间。在这项工作中,我们提出了一种混合深度对抗性自动编码器(VAE-GAN)来集成生成对抗网络(GAN)和变分自动编码器(VAE),用于预测空间和时间维度的参数化非线性流体流动。高维输入被卷积编码器中的非线性函数压缩成低维表示。通过这种方式,在卷积解码器中重建的预测流体流动包含高度非线性和混沌性质的动态流体流动物理。此外,将低维表示应用于对抗网络进行模型训练和参数优化,从而实现快速计算过程。混合 VAE-GAN 的能力通过改变流经气缸测试案例以及第二个水柱坍塌案例的输入来说明。数值结果表明,这种混合 VAE-GAN 成功捕获了时空流特征,CPU 速度提高了三个数量级。这些有希望的结果表明,混合 VAE-GAN 可以在未来研究工作中高效准确地预测复杂流中发挥关键作用。混合 VAE-GAN 的能力通过改变流经气缸测试案例以及第二个水柱坍塌案例的输入来说明。数值结果表明,这种混合 VAE-GAN 成功捕获了时空流特征,CPU 速度提高了三个数量级。这些有希望的结果表明,混合 VAE-GAN 可以在未来研究工作中高效准确地预测复杂流中发挥关键作用。混合 VAE-GAN 的能力通过改变流经气缸测试案例以及第二个水柱坍塌案例的输入来说明。数值结果表明,这种混合 VAE-GAN 成功捕获了时空流特征,CPU 速度提高了三个数量级。这些有希望的结果表明,混合 VAE-GAN 在未来的研究工作中可以在高效准确地预测复杂流方面发挥关键作用。
更新日期:2020-12-01
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