Surrogate models for multiregime flow problems

We investigate methods for mapping between low- and high-resolution simulations to generate surrogate models, which would significantly reduce the overall computational costs. Our focus is on interpolative decomposition, a rank-revealing matrix decomposition technique that efficiently selects key parameters to minimize the number of required simulation sets. We demonstrate that this approach remains effective even in the presence of multiple flow regimes (mode transitions) and show that the mapping process can also function as a classification tool for identifying different flow modes.