中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地理科学与资源研究所 [5]
力学研究所 [2]
自动化研究所 [2]
紫金山天文台 [2]
地质与地球物理研究所 [1]
长春光学精密机械与物... [1]
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OAI收割 [18]
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期刊论文 [17]
学位论文 [1]
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2024 [1]
2023 [6]
2022 [5]
2016 [2]
2015 [1]
2014 [3]
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Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods
期刊论文
OAI收割
GISCIENCE & REMOTE SENSING, 2024, 卷号: 61, 期号: 1, 页码: 32
作者:
Zhu, Wenquan
;
Xie, Zhiying
;
Zhao, Cenliang
;
Zheng, Zhoutao
;
Qiao, Kun
  |  
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2024/04/01
Gross primary production (GPP)
light use efficiency (LUE)
photosynthetic process
remote sensing
sun-induced chlorophyll fluorescence (SIF)
Adding attention to the neural ordinary differential equation for spatio-temporal prediction
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2023
作者:
Wang, Peixiao
;
Zhang, Tong
;
Zhang, Hengcai
;
Cheng, Shifen
;
Wang, Wangshu
  |  
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2023/12/04
Geospatial artificial intelligence
spatio-temporal prediction
spatio-temporal attention
neural ordinary differential equation
Physical interpretation of neural network-based nonlinear eddy viscosity models
期刊论文
OAI收割
AEROSPACE SCIENCE AND TECHNOLOGY, 2023, 卷号: 142, 页码: 13
作者:
Zhang, Xin-Lei
;
Xiao, Heng
;
Jee, Solkeun
;
He, Guowei
;
He GW(何国威)
  |  
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2023/11/28
Machine learning
Turbulence modeling
Ensemble Kalman inversion
Physical interpretability
LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP
期刊论文
OAI收割
JOURNAL OF CHEMINFORMATICS, 2023, 卷号: 15, 期号: 1, 页码: 13
作者:
Wang, Yitian
;
Xiong, Jiacheng
;
Xiao, Fu
;
Zhang, Wei
;
Cheng, Kaiyang
  |  
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2023/10/17
logD7.4
Lipid solubility
Graph neural network
Molecular property prediction
Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning
期刊论文
OAI收割
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS, 2023, 卷号: 21, 期号: 7, 页码: 18
作者:
Li, Wenbo
;
Liu, Libo
;
Chen, Yiding
;
Xiao, Zhuowei
;
Le, Huijun
  |  
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/01/02
deep learning
thermospheric mass density
CHAMP
feature extraction
solar radiation
geomagnetic storm
Review of explainable machine learning for anaerobic digestion
期刊论文
OAI收割
BIORESOURCE TECHNOLOGY, 2023, 卷号: 369, 页码: 10
作者:
Gupta, Rohit
;
Zhang, Le
;
Hou, Jiayi
;
Zhang, Zhikai
;
Liu, Hongtao
  |  
收藏
  |  
浏览/下载:0/0
  |  
提交时间:2023/05/19
Data -driven modelling
Sustainable waste management
Renewable energy
Bioenergy
Artificial intelligence
Review of explainable machine learning for anaerobic digestion
期刊论文
OAI收割
BIORESOURCE TECHNOLOGY, 2023, 卷号: 369, 页码: 10
作者:
Gupta, Rohit
;
Zhang, Le
;
Hou, Jiayi
;
Zhang, Zhikai
;
Liu, Hongtao
  |  
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2023/03/03
Data -driven modelling
Sustainable waste management
Renewable energy
Bioenergy
Artificial intelligence
DeFACT in ManuVerse for Parallel Manufacturing: Foundation Models and Parallel Workers in Smart Factories
期刊论文
OAI收割
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 页码: 12
作者:
Yang, Jing
;
Li, Shimeng
;
Wang, Xiaoxing
;
Lu, Jingwei
;
Wu, Huaiyu
  |  
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2023/02/22
Artificial systems
computational experiments
and parallel execution (ACP)
cyber-physical-social systems (CPSSs)
decentralized autonomous organization (DAO)
ManuVerse
parallel manufacturing
smart manufacturing
Estimating forces from cross-sectional data in the wake of flows past a plate using theoretical and data-driven models
期刊论文
OAI收割
PHYSICS OF FLUIDS, 2022, 卷号: 34, 期号: 11, 页码: 19
作者:
Tong, Wenwen
;
Wang SZ(王士召)
;
Yang, Yue
  |  
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2022/12/20
Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions
期刊论文
OAI收割
WATER RESOURCES RESEARCH, 2022, 卷号: 58, 期号: 10, 页码: 15
作者:
Huang, Sheng
;
Xia, Jun
;
Wang, Yueling
;
Wang, Wenyucheng
;
Zeng, Sidong
  |  
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2022/11/09
river modeling
machine learning
hydrodynamic models
water exchange
downstream boundary conditions