Exploring Physical Latent Spaces for High-Resolution Flow Restoration
This work addresses a domain-specific problem in computational fluid dynamics by enabling more accurate and efficient high-resolution flow simulations, though it is incremental as it builds on existing reduced representation methods.
The paper tackles the challenge of preserving small-scale features in long-term fluid flow simulations by using physics-based latent spaces as tools for neural networks, resulting in improved performance for tasks like turbulence and smoke plume restoration.
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for a neural network. In contrast to previous work, this paper treats the degrees of freedom of the simulated space purely as tools to be used by the neural network. We demonstrate this concept for learning reduced representations, as it is extremely challenging to faithfully preserve correct solutions over long time-spans with traditional reduced representations, particularly for solutions with large amounts of small scale features. This work focuses on the use of such physical, reduced latent space for the restoration of fine simulations, by training models that can modify the content of the reduced physical states as much as needed to best satisfy the learning objective. This autonomy allows the neural networks to discover alternate dynamics that significantly improve the performance in the given tasks. We demonstrate this concept for various fluid flows ranging from different turbulence scenarios to rising smoke plumes.