Physics-informed neural networks for learning fluid flows with symmetry

Kim, Younghyeon; Kwak, Hyungyeol; Nam, Jaewook

doi:10.1007/s11814-023-1420-4

Korean J. Chem. Eng.

2023

DOI: 10.1007/s11814-023-1420-4

|View full text |Cite

Physics-informed neural networks for learning fluid flows with symmetry

Younghyeon Kim

Hyungyeol Kwak

Jaewook Nam

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article3

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A deep learning framework for solving forward and inverse problems of power-law fluids

Zhai,

Yin,

Pang

2023

Physics of Fluids

View full text Add to dashboard Cite

We for the first time leverage deep learning approaches to solve forward and inverse problems of two-dimensional laminar flows for power-law fluids. We propose a deep-learning framework, called Power-Law-Fluid-Net (PL-Net). We develop a surrogate model to solve the forward problems of the power-law fluids, and solve the inverse problems utilizing only a small set of measurement data under the assumption that boundary conditions (BCs) can be partially known. In the design of the methods, we incorporate the hard boundary condition constraints to accelerate the iteration of stochastic gradient descent methods for minimizing loss functions. For the forward problems, by incorporating the constitutive parameters into the input variables of neural networks, the PL-Net serves as a surrogate model for simulating the pressure-driven flows inside pipes having cross sections of varying shapes. We investigate the influences of the BC type, activation function type, and number of collocation points on the accuracy of numerical solutions. For the inverse problems, the PL-Net infers the physical quantities or constitutive parameters from a small number of measurements of flow field variables. The BCs of the inverse problems can even be partially known. We demonstrate the effects of BC type, number of sensors, and noise level on accuracy of inferred quantities. Computational examples indicate the high accuracy of the PL-Net in tackling both the forward and inverse problems of the power-law fluids.

show abstract

A deep learning framework for solving forward and inverse problems of power-law fluids

Zhai,

Yin,

Pang

2023

Physics of Fluids

View full text Add to dashboard Cite

show abstract

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Zhao,

Zhang,

Lou

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems.

show abstract

Optimizing a Physics-Informed Machine Learning Model for Pulsatile Shear-Thinning Channel Flow

Son,

Park,

Kwak

et al. 2024

J. Soc. Rheol., Jpn

Self Cite

View full text Add to dashboard Cite

This paper explores the application of physics-informed neural networks for solving pulsatile shear-thinning flows in a two-dimensional channel. To identify an optimal model, models of varying implementations of boundary conditions, network sizes, number of training points, activation functions, and loss weights are investigated through case by case studies complemented by Gaussian-processes based Bayesian optimization. The final model demonstrates a high level of agreement with a reference numerical solution, with an error of less than 2%. This result indicates that appropriately trained PINNs can be utilized as a method for simulating transient shear-thinning flows.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Physics-informed neural networks for learning fluid flows with symmetry

Cited by 3 publications

References 21 publications

A deep learning framework for solving forward and inverse problems of power-law fluids

A deep learning framework for solving forward and inverse problems of power-law fluids

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Optimizing a Physics-Informed Machine Learning Model for Pulsatile Shear-Thinning Channel Flow

Contact Info

Product

Resources

About