Scientific machine learning for modeling and simulating complex fluids.

ABSTRACT: The formulation of rheological constitutive equations-models that relate internal stresses and deformations in complex fluids-is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-dri

SUBMITTER: Lennon KR 

PROVIDER: S-EPMC10318955 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature