Skip to Main Content
Keywords: Physics-informed neural network
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Journal Articles

Unsteady oblique stagnation-point flow of Maxwell fluid over an oscillating vertical plate: a physics-informed neural networks and homotopy analysis method comparative study

Available to Purchase
Yu Bai, Ya Gao, Yan Zhang, Chunyan Liu
Journal: International Journal of Numerical Methods for Heat & Fluid Flow
International Journal of Numerical Methods for Heat & Fluid Flow 1–28.
https://doi.org/10.1108/HFF-01-2026-0080
Published: 23 April 2026
... of the present study. In recent years, deep learning technologies have achieved unprecedented development, among which the physics-informed neural networks (PINN) proposed by Raissi et al. (2019) has garnered extensive attention in the field of fluid mechanics. Uddin et al. (2023...
View article titled, Unsteady oblique stagnation-point flow of Maxwell fluid over an oscillating vertical plate: a physics-informed neural networks and homotopy analysis method comparative study
Open the PDF for Unsteady oblique stagnation-point flow of Maxwell fluid over an oscillating vertical plate: a physics-informed neural networks and homotopy analysis method comparative study in another window
Add to Citation Manager for Unsteady oblique stagnation-point flow of Maxwell fluid over an oscillating vertical plate: a physics-informed neural networks and homotopy analysis method comparative study
Journal Articles

Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion

Open Access
En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen, Shuo Hao
Journal: International Journal of Numerical Methods for Heat & Fluid Flow
International Journal of Numerical Methods for Heat & Fluid Flow (2024) 34 (1): 131–149.
https://doi.org/10.1108/HFF-05-2023-0239
Published: 22 November 2023
...En-Ze Rui; Guang-Zhi Zeng; Yi-Qing Ni; Zheng-Wei Chen; Shuo Hao Purpose Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode...
View article titled, Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion
Open the PDF for Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion in another window
Add to Citation Manager for Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion
This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal

Gift article access

As a benefit of your subscription, you can share temporary access to restricted articles.

Each link will stop working after 30 days or 10 uses. You may create up to 10 links in a 30 day period.

Please sign in to your personal account to gift article access.

Register

Gift article access

As a benefit of your subscription, you can share temporary access to restricted articles.

Each link will stop working after 30 days or 10 uses. You may create up to 10 links in a 30 day period.

Gift articles remaining: --

Gift article access

Each link will stop working after 30 days or 10 uses. You may create up to 10 links in a 30 day period.

Gift articles remaining: --

Gift article access

As a benefit of your subscription, you can share temporary access to restricted articles.

Each link will stop working after 30 days or 10 uses.

You have reached the limit of 10 links within a 30 day period.

Sharing Unavailable

Sharing content requires targeting cookies to be enabled. Please update your cookie preferences to use this feature.