Parameter-Conditioned Sequential Generative Modeling of Fluid Flows
This addresses the problem of expensive fluid dynamics simulations for researchers and engineers, offering a significant speed-up but is incremental as it builds on existing generative modeling concepts.
The paper tackles the high computational cost of fluid flow simulations by introducing a neural network method for parameterized simulations, achieving orders of magnitude faster simulations while capturing local and global flow properties across various conditions.
The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for learning neural network models capable of performing efficient parameterized simulations of fluid flows. Evaluated on their ability to simulate both two-dimensional and three-dimensional fluid flows, trained models are shown to capture local and global properties of the flow fields at a wide array of flow conditions. Furthermore, flow simulations generated by the trained models are shown to be orders of magnitude faster than the corresponding computational fluid dynamics simulations.