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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 [4]
自动化研究所 [4]
烟台海岸带研究所 [2]
成都山地灾害与环境研... [1]
理论物理研究所 [1]
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OAI收割 [23]
内容类型
期刊论文 [21]
会议论文 [2]
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2024 [4]
2023 [5]
2022 [1]
2021 [3]
2020 [3]
2019 [2]
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Materials ... [1]
Mechanics [1]
Physics [1]
Physics, C... [1]
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Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions
期刊论文
OAI收割
ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 卷号: 179, 页码: 22
作者:
Jiang, Hu
;
Zou, Qiang
;
Zhu, Yunqiang
;
Li, Yong
;
Zhou, Bin
  |  
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2024/10/08
Spatiotemporal prediction
Rainfall-driven debris flow
Deep learning
Machine learning
Rainfall features
Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions
期刊论文
OAI收割
ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 卷号: 179, 页码: 22
作者:
Jiang, Hu
;
Zou, Qiang
;
Zhu, Yunqiang
;
Li, Yong
;
Zhou, Bin
  |  
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2024/08/14
Spatiotemporal prediction
Rainfall-driven debris flow
Deep learning
Machine learning
Rainfall features
Top-down constitutive modelling to capture nanoscale shear localization
期刊论文
OAI收割
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS, 2024, 卷号: 187, 页码: 17
作者:
Wen JC(温济慈)
;
Wei YJ(魏宇杰)
  |  
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2024/06/11
Deformation localization
Physics -Driven Machine Learning
Constitutive Model
Nanoscale shear bands
Clustering dimensionless learning for multiple-physical-regime systems
期刊论文
OAI收割
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2024, 卷号: 420, 页码: 21
作者:
Zhang, Lei
;
Xu, Zhaoyue
;
Wang, Shizhao
;
He, Guowei
;
He GW(何国威)
  |  
收藏
  |  
浏览/下载:43/0
  |  
提交时间:2024/03/11
Cluster
Active subspace
Data-driven dimensional analysis
Machine learning
Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters
期刊论文
OAI收割
REMOTE SENSING, 2023, 卷号: 15, 期号: 10
作者:
Chen, Hao
;
Lin, Xingwen
;
Sun, Yibo
;
Wen, Jianguang
;
Wu, Xiaodan
  |  
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2023/07/03
Sentinel-2
albedo
data-driven machine learning algorithms
remote sensing
Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches
期刊论文
OAI收割
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 卷号: 871, 页码: 11
作者:
Wang, Xiaoqing
;
Li, Fei
;
Teng, Yuefa
;
Ji, Chenglong
;
Wu, Huifeng
  |  
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2024/01/26
Toxic effects
Machine learning
Risk assessment
Mechanism -driven analysis
TiO2-based engineered nanoparticles(nTiO2)
Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches
期刊论文
OAI收割
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 卷号: 871, 页码: 11
作者:
Wang, Xiaoqing
;
Li, Fei
;
Teng, Yuefa
;
Ji, Chenglong
;
Wu, Huifeng
  |  
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2024/11/08
Toxic effects
Machine learning
Risk assessment
Mechanism -driven analysis
TiO2-based engineered nanoparticles(nTiO2)
Machine learning versus crop growth models: an ally, not a rival
期刊论文
OAI收割
AOB PLANTS, 2023, 卷号: 15, 期号: 2, 页码: 7
作者:
Zhang, Ningyi
;
Zhou, Xiaohan
;
Kang, Mengzhen
;
Hu, Bao-Gang
;
Heuvelink, Ep
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2023/11/17
Knowledge
and data-driven modelling
Machine learning
Process-based models
yield prediction
Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality
期刊论文
OAI收割
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 6, 页码: 1445-1461
作者:
Xiaoyu Jiang
;
Xiangyin Kong
;
Zhiqiang Ge
  |  
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2023/05/29
Curse of dimensionality
data augmentation
data-driven modeling
industrial processes
machine learning
Artificial neural network based response surface for data-driven dimensional analysis
期刊论文
OAI收割
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 卷号: 459, 页码: 19
作者:
Xu, Zhaoyue
;
Zhang, Xinlei
;
Wang, Shizhao
;
He, Guowei
;
He GW(何国威)
  |  
收藏
  |  
浏览/下载:56/0
  |  
提交时间:2022/07/18
Artificial neural network
Response surface
Data-driven dimensional analysis
Machine learning
Fluid-structure interaction