A Navier–Stokes-Informed Neural Network for Simulating the Flow Behavior of Flowable Cement Paste in 3D Concrete Printing
In this work, we propose a Navier–Stokes-Informed Neural Network (NSINN) as a surrogate approach to predict the localized flow behavior of cementitious materials for advancing 3D additive construction technology to gain fundamental insights into multiscale mechanisms of cement paste rheology. NS equ...
Saved in:
Main Authors: | Tianjie Zhang, Donglei Wang, Yang Lu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Buildings |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-5309/15/2/275 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Navier-Stokes: Singularities and Bifurcations
by: Rômulo Damasclin Chaves dos Santos, et al.
Published: (2024-10-01) -
The topological degree method for equations of the Navier-Stokes type
by: V. T. Dmitrienko, et al.
Published: (1997-01-01) -
Controllability of a fluid-structure interaction system coupling the Navier–Stokes system and a damped beam equation
by: Buffe, Rémi, et al.
Published: (2023-11-01) -
On the global well-posedness and exponential stability of 3D heat conducting incompressible Navier-Stokes equations with temperature-dependent coefficients and vacuum
by: Jianxia He, et al.
Published: (2024-09-01) -
Shannon Entropy Computations in Navier–Stokes Flow Problems Using the Stochastic Finite Volume Method
by: Marcin Kamiński, et al.
Published: (2025-01-01)