  In recent years, there has been an explosion of machine learning techniques
for turbulence closure modeling, though many rely on augmenting existing
models. While this has proven successful in single-phase flows, it breaks down
for multiphase flows, particularly when the system dynamics are controlled by
two-way coupling between the phases. In this work, we propose an approach that
blends sparse regression and gene expression programming (GEP) to generate
closed-form algebraic models from simulation data. Sparse regression is used to
determine a minimum set of functional groups required to capture the physics
and GEP is used to automate the formulation of the coefficients and
dependencies on operating conditions. The framework is demonstrated on a
canonical gas--solid flow in which two-way coupling generates and sustains
fluid-phase turbulence.
