DeePN$^2$: A deep learning-based non-Newtonian hydrodynamic model
This addresses the challenge of reliable and interpretable hydrodynamic models for non-Newtonian flows, which is important for researchers in fluid dynamics and materials science, though it appears incremental by extending to more complex micro-structural models.
The paper tackles the problem of modeling non-Newtonian hydrodynamics for polymeric flows by developing DeePN^2, a deep learning-based model that systematically transfers micro-scale polymer dynamics to macro-scale hydrodynamics, showing it can capture viscoelastic differences from complex molecular structures without human intervention.
A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN$^2$, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN$^2$ using more complex micro-structural models. We show that DeePN$^2$ can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention.