@Article{JML-1-1,
author = {Lidong, Fang and Pei, Ge and Zhang, Lei and Weinan, E and Lei, Huan},
title = {DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model},
journal = {Journal of Machine Learning},
year = {2022},
volume = {1},
number = {1},
pages = {114--140},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2790-2048},
doi = {https://doi.org/10.4208/jml.220115},
url = {https://global-sci.com/article/87608/deepn2-a-deep-learning-based-non-newtonian-hydrodynamic-model}
}