Abstract We introduce a deep learning method to simulate the chaotic motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The Lagrangian trajectories of particles in flame were captured using a high-speed camera and subsequently reconstructed in a 3-dimensional cylindrical space. These experimentally determined trajectories were next used to train the two most widely adopted deep generative models – the variational autoencoder (VAE) and the generative adversarial net (GAN). The performance of both models was then benchmarked according to statistical analysis performed on both the simulated trajectories and the ground truth, regarding accuracy and generalization criteria. Our results show that the GAN model produces non-repeating trajectories precisely capturing the statistical features of those determined in experiments, as revealed in their spatial-temporal scaling relationship and linear pair-correlation values; whereas the VAE model trained with the same amount of data tends to overfit, producing inferior results grappling with the trade-off between accuracy and generalization criteria. The influence of the size of training data on model performance is also evaluated. This paper concludes with a discussion on the potential utility of the deep learning enabled trajectory simulation.

中文翻译：

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深度学习启用拉格朗日粒子轨迹模拟

摘要 我们引入了一种深度学习方法来模拟被困在反浮力火焰再循环区中的粒子的混沌运动。火焰中粒子的拉格朗日轨迹是使用高速相机捕获的，随后在 3 维圆柱空间中重建。这些实验确定的轨迹接下来被用来训练两个最广泛采用的深度生成模型——变分自编码器 (VAE) 和生成对抗网络 (GAN)。然后根据对模拟轨迹和地面实况进行的统计分析，在准确性和泛化标准方面对两种模型的性能进行基准测试。我们的结果表明，GAN 模型产生的非重复轨迹精确地捕捉了实验中确定的轨迹的统计特征，正如它们的时空尺度关系和线性对相关值所揭示的那样；而用相同数量的数据训练的 VAE 模型往往会过度拟合，产生较差的结果，需要在准确性和泛化标准之间进行权衡。还评估了训练数据大小对模型性能的影响。本文最后讨论了启用深度学习的轨迹模拟的潜在效用。产生较差的结果，在准确性和泛化标准之间进行权衡。还评估了训练数据大小对模型性能的影响。本文最后讨论了启用深度学习的轨迹模拟的潜在效用。产生较差的结果，在准确性和泛化标准之间进行权衡。还评估了训练数据大小对模型性能的影响。本文最后讨论了启用深度学习的轨迹模拟的潜在效用。