Learning constitutive models and rheology from partial flow measurements

Abstract

Constitutive laws relate fluid stress to deformation and underpin predictions of non-Newtonian behavior in industrial and biological fluids. Standard characterization relies on measurements in idealized flows that often miss physics relevant to complex geometries. Existing data-driven methods overfit sparse data, lack geometry portability, or presuppose constitutive forms. To unify measurement and constitutive discovery, we developed an end-to-end framework that leverages automatic differentiation through a full physics simulation. By embedding a frame-invariant tensor basis neural network (TBNN) within a differentiable non-Newtonian solver, we learn constitutive laws from any flow observable without presupposing a specific model, spanning generalized Newtonian, viscoelastic, and yield-stress behavior. Unlike coordinate-dependent methods, learning local material response enables accurate flow predictions in unseen geometries and conditions without retraining. We then distill the TBNN closure into symbolic form via automated model selection using the Bayesian Information Criterion, extracting interpretable physical parameters. This work establishes a foundation for comprehensive characterization of complex fluids directly within their operating environment ("digital rheometry") with broad applicability to constitutive discovery across engineering and the physical sciences.