Signed distance function-biased flow importance sampling for implicit neural compression of flow fields

Abstract

The rise of exascale supercomputing has motivated an increase in high-fidelity computational fluid dynamics (CFD) simulations. The detail in these simulations, often involving shape-dependent, time-variant flow domains and low-speed, complex, turbulent flows, is essential for fueling innovations in fields like wind, civil, automotive, or aerospace engineering. However, the massive amount of data these simulations produce can overwhelm storage systems and negatively affect conventional data management and postprocessing workflows, including iterative procedures such as design space exploration, optimization, and uncertainty quantification. This study proposes a novel sampling method harnessing the signed distance function (SDF) concept, SDF-biased flow importance sampling (BiFIS) and implicit compression based on implicit neural network representations for transforming large-size, shape-dependent flow fields into reduced-size shape-agnostic images. Designed to alleviate the above-mentioned problems, our approach achieves near-lossless compression ratios of approximately 17000:1, reducing the size of a bridge aerodynamics forced-vibration simulation from roughly 600 GB to about 36 MB while maintaining low reproduction errors, in most cases below 0.5% which is unachievable with other sampling approaches. Our approach also allows for real-time analysis and visualization of these massive simulations and does not involve decompression preprocessing steps that yield full simulation data again. Given that image sampling is a fundamental step for any image-based flow field prediction model, the proposed BiFIS method can significantly improve the accuracy and efficiency of such models, helping any application that relies on precise flow field predictions. The BiFIS code is available on GitHub.

Omar A. Mures

Instructor

My research interests include Deep Learning, Computer Vision and Computer Graphics.