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.
