MS 1: Data-driven progress in modeling multi-scale and multi-physics problem

Multiphase flow problems occur in many natural systems and technical applications. These systems challenge theoretical models due to the broad range of systems with interaction, inside or adjacent to porous media, or in free-flow domains, ranging from creeping flow to highly turbulent flow. Additionally, interfacial effects such as mass, momentum, or energy transfer determine the large-scale system dynamics. Purely physics-based modeling for such flow problems has arrived at a substantial barrier for progress in modeling. In this minisymposium we bring together leading researchers to discuss progress in the mathematical modeling of compressible multi-phase flow, for data-integrated approaches based on experimental results as well as how machine learning can be used to substitute parts of computational fluid dynamics solvers.