Rheo-SINDy: Finding a constitutive model from rheological data for complex fluids using sparse identification for nonlinear dynamics

Abstract

Rheology plays a pivotal role in understanding the flow behavior of fluids by discovering governing equations that relate deformation and stress, known as constitutive equations. Despite the importance of these equations, current methods for deriving them lack a systematic methodology, often relying on sense of physics and incurring substantial costs. To overcome this problem, we propose a novel method named Rheo-SINDy, which employs the sparse identification of nonlinear dynamics (SINDy) algorithm for discovering constitutive models from rheological data. Rheo-SINDy was applied to five distinct scenarios, four with well-established constitutive equations, and one without predefined equations. Our results demonstrate that Rheo-SINDy successfully identified accurate models for the known constitutive equations and derived physically plausible approximate models for the scenario without established equations. Notably, the identified approximate models can accurately reproduce nonlinear shear rheological properties, especially at steady state, including shear thinning. These findings validate the availability of Rheo-SINDy in handling data complexities and underscore its potential for advancing the development of data-driven approaches in rheology. Nevertheless, further refinement of learning strategies is essential for enhancing robustness to fully account for the complexities of real-world rheological data.