  Multi-fluid flows are found in various industrial processes, including metal
injection molding and 3D printing. The accuracy of multi-fluid flow modeling is
determined by how well interfaces and capillary forces are represented. In this
paper, the multi-fluid flow problem is discretized using a combination of a
Physics-Informed Neural Network (PINN) with a finite element discretization. To
determine the best PINN formulation, a comparative study is conducted using a
manufactured solution. We compare interface reinitialization methods to
determine the most suitable approach for our discretization strategy. We devise
a neural network architecture that better handles complex free surface
topologies. Finally, the coupled numerical strategy is used to model a rising
bubble problem.
