Dye advection without the blur: ML-based flow visualization

Abstract

Semi-Lagrangian texture advection (SLTA) enables efficient visualization of 2D and 3D unsteady flow. The major drawback of SLTA-based visualizations is numerical diffusion caused by iterative texture interpolation. We focus on reducing numerical diffusion in techniques that use textures sparsely populated by solid blobs, such as typically in dye advection. A ReLU-based model architecture is the foundation of our ML-based approach. Multiple model configurations are trained to learn a performant interpolation model that reduces numerical diffusion. Our evaluation investigates the models’ ability to generalize concerning the flow and length of the advection process. The model with the best tradeoff between the computational effort to compute, quality of the result, and generality of application is found to be single-layer ReLU-based. This model is further analyzed and explained in-depth and improved using symmetry constraints. Additionally, a metamodel is fitted to predict single-layer ReLU model parameters for advection processes of any length. The metamodel removes the need for any prior training when applying our technique to a new scenario. Additionally, we show that our model is compatible with Back and Forth Error Compensation and Correction to improve the quality of the advection result further. We demonstrate that our model shows excellent diffusion reduction properties in typical examples of 3D steady and unsteady flow visualization. Finally, we utilize the strong diffusion reduction capabilities of our model to compute dye advection with exponential decay, a novel method that we introduce to visualize the extent and evolution of unsteadiness in both 2D and 3D unsteady flow.