亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

人工神经网络 人工智能 深度学习 计算机科学 物理
作者
Salah A. Faroughi,Nikhil M. Pawar,Célio Fernandes,Maziar Raissi,Subasish Das,Nima K. Kalantari,Seyed Kourosh Mahjour
出处
期刊:Journal of Computing and Information Science in Engineering [ASME International]
卷期号:: 1-45 被引量:15
标识
DOI:10.1115/1.4064449
摘要

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multi-physics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multi-physics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
10秒前
liuyuannzhuo发布了新的文献求助10
14秒前
27秒前
DayFu完成签到 ,获得积分10
37秒前
44秒前
50秒前
50秒前
liuyuannzhuo发布了新的文献求助10
1分钟前
1分钟前
CQU科研萌新完成签到,获得积分10
1分钟前
隐形曼青应助CQU科研萌新采纳,获得10
1分钟前
Singularity应助tlx采纳,获得20
1分钟前
1分钟前
上官若男应助执着夏山采纳,获得10
1分钟前
1分钟前
2分钟前
2分钟前
充电宝应助执着夏山采纳,获得10
2分钟前
2分钟前
3分钟前
良辰应助科研通管家采纳,获得10
3分钟前
3分钟前
甜蜜发带完成签到 ,获得积分10
3分钟前
4分钟前
执着夏山发布了新的文献求助10
4分钟前
4分钟前
一墨完成签到,获得积分10
4分钟前
4分钟前
清爽夜雪完成签到,获得积分10
4分钟前
从容栾发布了新的文献求助10
4分钟前
科研搬运工完成签到,获得积分10
4分钟前
无花果应助Demi_Ming采纳,获得10
5分钟前
5分钟前
脑洞疼应助科研通管家采纳,获得10
5分钟前
良辰应助科研通管家采纳,获得10
5分钟前
5分钟前
Demi_Ming发布了新的文献求助10
5分钟前
5分钟前
5分钟前
6分钟前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
叶剑英与华南分局档案史料 500
Foreign Policy of the French Second Empire: A Bibliography 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3146739
求助须知:如何正确求助?哪些是违规求助? 2798061
关于积分的说明 7826588
捐赠科研通 2454566
什么是DOI,文献DOI怎么找? 1306394
科研通“疑难数据库(出版商)”最低求助积分说明 627708
版权声明 601527