Abstract

Flows used to process complex soft materials almost always involve complex deformations that cannot be captured in a rheometer but may profoundly influence the final microstructure and performance of the material.  Furthermore, accurate first-principles models to relate flow, microstructure, and stress are unavailable for most complex fluids, especially when undergoing complex deformations. We describe a framework that uses machine learning and data assimilation to circumvent these limitations, exploiting a new experimental approach from the research group of Matt Helgeson that yields microstructural information in complex flows of complex fluids. The framework is constructed to automatically satisfy the key symmetry of microstructural evolution, material frame indifference, and enables data-driven determination of microstructural evolution equations for complex fluids in very general flows.