Learning Unknown Physics of non-Newtonian Fluids

Abstract

We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.

References in corpus (3)

• Understanding and mitigating gradient pathologies in physics-informed neural networks
• Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
• A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

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