Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the Double Gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing and informs hyperparameter settings. Overall, we find our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU.
翻译:计算流体动态模拟所产生的时间变化矢量字段往往过于庞大,对准确的互动分析和探索构成挑战。为了应对这些挑战,对拉格兰加方表示法的减少进行了越来越多的研究,作为提高科学时间变化矢量实地勘探能力的一种手段。本文介绍了一种新型的深神经网络粒子追踪方法,以探索以拉格兰加方流图为代表的时间变化矢量字段。在我们工作流程中,首先利用实地处理提取拉格兰基亚流图,然后利用提取的数据来学习流流场行为。使用经过培训的模型来预测新的粒子轨迹提供了固定的小型记忆足迹和快速推断。为了展示和评估拟议方法,我们利用众所周知的分析数据集“双基”来进行一项深入的绩效研究。我们的研究考虑了两种流动地图提取战略以及培训样本和整合期限对功效的影响,评估了用于培训和测试的多倍数抽样选项,然后用于学习流体行为行为。我们发现,我们的方法需要使用一个固定的存储秒数,即固定的粒子粒子跟踪和快速推断。我们用10MB的精确度,然后用一个固定的存储秒来计算模型,然后在每条直径测测测的轨道的轨道的轨道上,然后为拉基的精确地计算。