TSR-VFD: Generating temporal super-resolution for unsteady vector field data

We present TSR-VFD, a novel deep learning solution that recovers temporal super-resolution (TSR) of three-dimensional vector field data (VFD) for unsteady flow. In scientific visualization, TSR-VFD is the first work that leverages deep neural nets to interpolate intermediate vector fields from temporally sparsely sampled unsteady vector fields. The core of TSR-VFD lies in using two networks: InterpolationNet and MaskNet, that process the vector components of different scales from sampled vector fields as input and jointly output synthesized intermediate vector fields. To demonstrate our approach's effectiveness, we report qualitative and quantitative results with several data sets and compare TSR-VFD against vector field interpolation using linear interpolation (LERP), generative adversarial network (GAN), and recurrent neural network (RNN). In addition, we compare TSR-VFD with a lossy compression (LC) scheme. Finally, we conduct a comprehensive study to evaluate critical parameter settings and network designs.