Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning

Abstract

We introduce Scalar2Vec, a new deep learning solution that translates scalar fields to velocity vector fields for scientific visualization. Given multivariate or ensemble scalar field volumes and their velocity vector field counterparts, Scalar2Vec first identifies suitable variables for scalar-to-vector translation. It then leverages a k-complete bipartite translation network (kCBT-Net) to complete the translation task. kCBT-Net takes a set of sampled scalar volumes of the same variable as input, extracts their multi -scale information, and learns to synthesize the corresponding vector volumes. Ground-truth vector fields and their derived quantities are utilized for loss computation and network training. After training, Scalar2Vec can infer unseen velocity vector fields of the same data set directly from their scalar field counterparts. We demonstrate the effectiveness of Scalar2Vec with quantitative and qualitative results on multiple data sets and compare it with three other state-of-the-art deep learning methods.