Multi-Viscosity Physics-Informed Neural Networks for Generating Ultra High Resolution Flow Field Data-Reference-Cited by-同舟云学术

Multi-Viscosity Physics-Informed Neural Networks for Generating Ultra High Resolution Flow Field Data

Published:2023-04-21 Issue:4 Volume:37 Page:279-297
ISSN:1061-8562
Container-title:International Journal of Computational Fluid Dynamics
language:en
Short-container-title:International Journal of Computational Fluid Dynamics

Author:

Zhang Sen¹,Guo Xiao-Wei¹,Li Chao¹,Zhao Ran¹,Yang Canqun¹²³,Wang Wei²³,Zhong Yanxu²³

Affiliation:

1. Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, P. R. China

2. Haihe Lab of ITAI, West Middle Ring Expy and Binyu Ave Intersection Northwest, Tianjin, P. R. China

3. National SuperComputer Center in Tianjin, Tianjin, P. R. China

Funder

Haihe Lab of ITAI

National Natural Science Foundation of China

National University of Defense Technology Foundation

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Mechanics of Materials,Energy Engineering and Power Technology,Condensed Matter Physics,Aerospace Engineering,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618562.2023.2295286

Reference45 articles.

1. Prediction of porous media fluid flow using physics informed neural networks

2. Uncovering near-wall blood flow from sparse data with physics-informed neural networks

3. Ayachit, U.. 2015. The Paraview Guide: A Parallel Visualization Application. Clifton Park, NY: Kitware, Inc.

4. Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains

5. Physics-informed neural networks (PINNs) for fluid mechanics: a review