One-shot Deep Learning Pressure Solver for 2D Eulerian Smoke Simulation Application

In the realm of Eulerian-based fluid dynamics simulations, the computational demands associated with the number of the projection steps, particularly the management of the extensive linear system stemming from the Gauss-Seidel equation, pose a significant temporal and computational burden. In this research paper, we propose a custom Deep Learning (DL) approach to the projection method leveraging machine learning paradigms, specifically integrating novel Deep Neural Networks (DNN) into a custom made 2D smoke simulator. Proposed solution offers a high quality output, comparable with naive techniques, while using less computational memory and fraction of solving time. The effectiveness of our proposed approach has been validated through testing across a spectrum of smoke scenes, chosen to substantially differ from the training dataset. The demonstrated outcomes underscore the accelerated computational performance and extrapolation capabilities inherent in our method, thus highlighting its adaptability and robustness in addressing simulation scenarios beyond the training dataset parameters.