DeePN$^2$: A deep learning-based non-Newtonian hydrodynamic model

• URL: http://arxiv.org/abs/2112.14798v1
• Date: Wed, 29 Dec 2021 19:30:07 GMT
• Title: DeePN$^2$: A deep learning-based non-Newtonian hydrodynamic model
• Authors: Lidong Fang, Pei Ge, Lei Zhang, Huan Lei, Weinan E
• Abstract summary: 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. We show that DeePN$2$ can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention.
• Score: 16.629659193663713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A long standing problem in the modeling of non-Newtonian hydrodynamics 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 carry out further study of 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.

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