# DeePN2: A deep learning-based non-Newtonian hydrodynamic model

arXiv: https://arxiv.org/abs/2112.14798 · HTML: https://mlsanity.com/paper/2112.14798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 77 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Data Driven Governing Equations Approximation Using Deep Neural Networks](https://mlsanity.com/paper/1811.05537/) (2018)
- [Extracting Sparse High-Dimensional Dynamics from Limited Data](https://mlsanity.com/paper/1707.08528/) (2017)
- [OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle](https://mlsanity.com/paper/2009.02327/) (2020)
- [Data-driven parameterization of the generalized Langevin equation](https://mlsanity.com/paper/1606.02596/) (2016)
- [Particle-based Energetic Variational Inference](https://mlsanity.com/paper/2004.06443/) (2020)
- [Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows](https://mlsanity.com/paper/2009.13415/) (2020)
- [Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids](https://mlsanity.com/paper/2103.06685/) (2021)

## Built on by (most cited first, 1 of 1)

- [On the generalization ability of coarse-grained molecular dynamics models for non-equilibrium processes](https://mlsanity.com/paper/2409.11519/) (2024)

## Abstract (arXiv)

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. DeePN2, 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 DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention.