气候变化领域集成服务门户(CCPortal): Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

CCPortal

DOI	10.1126/science.aaw4741
	Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
	Raissi M.; Yazdani A.; Karniadakis G.E.
发表日期	2020
ISSN	0036-8075
起始页码	1026
结束页码	1030
卷号	367 期号:6481
英文摘要	For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible. HFM is robust to low resolution and substantial noise in the observation data, which is important for potential applications. © 2020 American Association for the Advancement of Science. All rights reserved.
关键词	fluid mechanics machine learning Navier-Stokes equations pressure field velocity visualization article learning noise quantitative analysis velocity
语种	英语
来源机构	Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/133679
推荐引用方式 GB/T 7714	Raissi M.,Yazdani A.,Karniadakis G.E.. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations[J]. Science,2020,367(6481).
APA	Raissi M.,Yazdani A.,&Karniadakis G.E..(2020).Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.,367(6481).
MLA	Raissi M.,et al."Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations".367.6481(2020).