Learning Physical Parameters and Detail Enhancement for Gaseous Scene Design Based on Data Guidance

This article articulates a novel learning framework for both parameter estimation and detail enhancement for Eulerian gas based on data guidance. The key motivation of this article is to devise a new hybrid, grid-based simulation that could inherit modeling and simulation advantages from both physically-correct simulation methods and powerful data-driven methods, while combating existing difficulties exhibited in both approaches. We first employ a convolutional neural network (CNN) to estimate the physical parameters of gaseous phenomena in Eulerian settings, then we can use the just-learnt parameters to re-simulate (with or without artists' guidance) for specific scenes with flexible coupling effects. Next, a second CNN is adopted to reconstruct the high-resolution velocity field to guide a fast re-simulation on the finer grid, achieving richer and more realistic details with little extra computational expense. From the perspective of physics-based simulation, our trained networks respect temporal coherence and physical constraints. From the perspective of the data-driven machine-learning approaches, our network design aims at extracting a meaningful parameters and reconstructing visually realistic details. Additionally, our implementation based on parallel acceleration could significantly enhance the computational performance of every involved module. Our comprehensive experiments confirm the controllability, effectiveness, and accuracy of our novel approach when producing various gaseous scenes with rich details for widespread graphics applications.