Robust feature extraction is an integral part of scientific visualization. In unsteady vector field analysis, researchers recently directed their attention towards the computation of near-steady reference frames for vortex extraction, which is a numerically challenging endeavor. In this paper, we utilize a convolutional neural network to combine two steps of the visualization pipeline in an end-to-end manner: the filtering and the feature extraction. We use neural networks for the extraction of a steady reference frame for a given unsteady 2D vector field. By conditioning the neural network to noisy inputs and resampling artifacts, we obtain numerically stabler results than existing optimization-based approaches. Supervised deep learning typically requires a large amount of training data. Thus, our second contribution is the creation of a vector field benchmark data set, which is generally useful for any local deep learning-based feature extraction. Based on Vatistas velocity profile, we formulate a parametric vector field mixture model that we parameterize based on numerically-computed example vector fields in near-steady reference frames. Given the parametric model, we can efficiently synthesize thousands of vector fields that serve as input to our deep learning architecture. The proposed network is evaluated on an unseen numerical fluid flow simulation.
翻译:在不稳定的矢量场分析中,研究人员最近将其注意力转向计算旋涡提取的近稳定参考框架,这是一个具有数字挑战性的努力。在本文件中,我们利用一个进化神经网络,以端到端的方式将可视化管道的两个步骤结合起来:过滤和特征提取。我们使用神经网络为给定的不稳定的 2D 矢量场提取一个稳定的参考框架。通过将神经网络调整为噪音输入和再生文物,我们获得了比现有优化方法更稳定的数值稳定的结果。超常的深层次学习通常需要大量的培训数据。因此,我们的第二个贡献是创建矢量场基准数据集,一般用于任何以深层次学习为基础的特征提取。根据瓦蒂斯塔斯速度剖析,我们开发了一种半量矢量场混合物模型,我们根据数字合成的样板在近固定的参照框中对它进行参数化。根据对数值模型,我们可以有效地合成一个用于输入我们深层次矢量流的流体。我们所拟议的一个模拟的流体,我们可以对一个用于输入我们深层次矢量的流体进行模拟。