TY - JOUR T1 - DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model AU - Fang , Lidong AU - Ge , Pei AU - Zhang , Lei AU - E , Weinan AU - Lei , Huan JO - Journal of Machine Learning VL - 1 SP - 114 EP - 140 PY - 2022 DA - 2022/03 SN - 1 DO - http://doi.org/10.4208/jml.220115 UR - https://global-sci.org/intro/article_detail/jml/20373.html KW - Non-Newtonian fluids, Machine learning, Multi-scale modeling, Fluid mechanics. AB -

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 microscale 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.