Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

Understand

We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning.

• More specifically, we target fluid flows, i.e.
• Navier-Stokes problems, and we propose a novel LSTM-based approach to predict the changes of pressure fields over time.
• The central challenge in this context is the high dimensionality of Eulerian space-time data sets.