Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids.

ABSTRACT: Reliable and accurate prediction of complex fluids' response under flow is of great interest across many disciplines, from biological systems to virtually all soft materials. The challenge is to solve non-trivial time and rate dependent constitutive equations to describe these structured fluids under various flow protocols. We present Rheology-Informed Neural Networks (RhINNs) for solving systems of Ordinary Differential Equations (ODEs) adopted for complex fluids. The proposed RhINNs are employed to solve the constitutive models with multiple ODEs by benefiting from Automatic Differentiation in neural networks. In a direct solution, the RhINNs platform accurately predicts the fully resolved solution of constitutive equations for a Thixotropic-Elasto-Visco-Plastic (TEVP) complex fluid for

SUBMITTER: Mahmoudabadbozchelou M 

PROVIDER: S-EPMC8187644 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature