论文标题
通过深度学习时间变化的标量合奏，加速概率行进立方体
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
论文作者
论文摘要
由于集合数据集的较大尺寸和多元和时间特征，可视化集成模拟的不确定性是具有挑战性的。研究合奏不确定性的一种流行方法是分析水平集的位置不确定性。概率游行立方体是一种对多变量高斯噪声分布进行蒙特卡洛采样的技术，以实现级别集合的位置不确定性可视化。但是，该技术遭受了较高的计算时间，因此无法实现交互式可视化和分析。本文介绍了一种基于深度学习的方法，用于学习具有多元高斯噪声假设的二维集合数据的级别不确定性。我们使用工作流程中时变的集合数据的前几个时间步骤训练模型。我们证明，我们训练的模型可以准确地不确定新的时间步骤的不确定性，并且比具有串行计算的原始概率模型的速度快于170倍，比原始平行计算快10倍。
Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.
