Physics-informed machine learning for particle stresses in dense suspensions

In experiments with dense suspensions it is relatively easy to measure particle positions and velocities. However, it is difficult or impossible to measure quantities such as the particle stresses. We use physics-informed Galerkin neural networks to learn the particle stresses by enforcing that the particle stresses, volume fractions, and fluxes satisfy the monodisperse or bidisperse suspension balance model. Results are validated with Force Coupling Method simulations of monodisperse and bidisperse suspensions, where the particle stress is available, to show that the learned stresses agree well with the simulation stresses. This work allows for uncovering unknown quantities that are unable to be measured from experimental results.