English | 繁體

Search in the results

UM

Browse/Search Results:  1-10 of 18 Help

Selected(0)Clear Items/Page:    Sort:
Generative broad Bayesian (GBB) imputer for missing data imputation with uncertainty quantification Journal article
Kuok, Sin Chi, Yuen, Ka Veng, Dodwell, Tim, Girolami, Mark. Generative broad Bayesian (GBB) imputer for missing data imputation with uncertainty quantification[J]. Knowledge-Based Systems, 2024, 301, 112272.
Authors:  Kuok, Sin Chi;  Yuen, Ka Veng;  Dodwell, Tim;  Girolami, Mark
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:7.2/7.4 | Submit date：2024/08/05
Bayesian Inference  Broad Bayesian Learning  Imputation  Missing Data  Uncertainty Quantification  
Bayesian time domain approach for damping identification and uncertainty quantification in stay cables using free vibration response Journal article
Feng, Zhouquan, Zhang, Jiren, Xuan, Xinyan, Wang, Yafei, Hua, Xugang, Chen, Zhengqing, Yan, Wangji. Bayesian time domain approach for damping identification and uncertainty quantification in stay cables using free vibration response[J]. Engineering Structures, 2024, 315, 118477.
Authors:  Feng, Zhouquan;  Zhang, Jiren;  Xuan, Xinyan;  Wang, Yafei;  Hua, Xugang; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:5.6/5.8 | Submit date：2024/08/05
Bayesian Inference  Damping Ratios  Free Vibration  Modal Identification  Stay Cables  Time Domain  Uncertainty Quantification  
Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics Journal article
Shuttleworth, Joseph G., Lei, Chon Lok, Whittaker, Dominic G., Windley, Monique J., Hill, Adam P., Preston, Simon P., Mirams, Gary R.. Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics[J]. Bulletin of Mathematical Biology, 2024, 86(1), 2.
Authors:  Shuttleworth, Joseph G.;  Lei, Chon Lok;  Whittaker, Dominic G.;  Windley, Monique J.;  Hill, Adam P.; et al.
Favorite | TC[WOS]:2 TC[Scopus]:4  IF:2.0/2.1 | Submit date：2024/02/22
Discrepancy  Experimental Design  Ion Channel  Mathematical Model  Misspecification  Uncertainty Quantification  
Machine Learning-Based Probabilistic Forecasting of Wind Power Generation: A Combined Bootstrap and Cumulant Method Journal article
Wan, Can, Cui, Wenkang, Song, Yonghua. Machine Learning-Based Probabilistic Forecasting of Wind Power Generation: A Combined Bootstrap and Cumulant Method[J]. IEEE Transactions on Power Systems, 2024, 39(1), 1370-1383.
Authors:  Wan, Can;  Cui, Wenkang;  Song, Yonghua
Favorite | TC[WOS]:6 TC[Scopus]:10  IF:6.5/7.4 | Submit date：2024/02/22
Bootstrap  Cumulant  Machine Learning  Probabilistic Forecasting  Uncertainty Quantification  Wind Power  
Physics-Informed Neural Networks for Settlement Analysis of the Immersed Tunnel of the Hong Kong-Zhuhai-Macau Bridge Journal article
He, Shu Yu, Zhou, Wan Huan, Tang, Cong. Physics-Informed Neural Networks for Settlement Analysis of the Immersed Tunnel of the Hong Kong-Zhuhai-Macau Bridge[J]. International Journal of Geomechanics, 2024, 24(1), 04023241.
Authors:  He, Shu Yu;  Zhou, Wan Huan;  Tang, Cong
Favorite | TC[WOS]:4 TC[Scopus]:6  IF:3.3/3.5 | Submit date：2024/02/22
Foundation Modulus  Multibeam Model  Physics-informed Neural Networks  Settlement  Uncertainty Quantification  
Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling Journal article
Kuok, Sin Chi, Yuen, Ka Veng. Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 419, 116680.
Authors:  Kuok, Sin Chi;  Yuen, Ka Veng
Favorite | TC[WOS]:4 TC[Scopus]:4  IF:6.9/6.7 | Submit date：2024/02/22
Bayesian Inference  Data-driven Modeling  Model Class Selection  Physical-inspired Modeling  Synergistic Metamodeling  Uncertainty Quantification  
A statistical influence line identification method using Bayesian regularization and a polynomial interpolating function Journal article
Zhi-Wei Chen, Long Zhao, Wang-Ji Yan, Ka-Veng Yuen, Chen Wu. A statistical influence line identification method using Bayesian regularization and a polynomial interpolating function[J]. Structural Control & Health Monitoring, 2022, 29(11), e3080.
Authors:  Zhi-Wei Chen;  Long Zhao;  Wang-Ji Yan;  Ka-Veng Yuen;  Chen Wu
Favorite | TC[WOS]:5 TC[Scopus]:5  IF:4.6/5.5 | Submit date：2022/08/29
Bayesian Analysis  Bil Identification  Regularization Technique  Structural Health Monitoring  Uncertainty Quantification  
Theoretical Methods and Application Prospects for Uncertainty Quantification in Distribution Network Operation Under the Influence of Stochastic Source-load Journal article
Han, Wang, Xiaoyuan, Xu, Zheng, Yan, Hongxun, Hui, Xiaotao, Fang. Theoretical Methods and Application Prospects for Uncertainty Quantification in Distribution Network Operation Under the Influence of Stochastic Source-load[J]. Journal of Global Energy Interconnection, 2022, 5(3), 230-241.
Authors:  Han, Wang;  Xiaoyuan, Xu;  Zheng, Yan;  Hongxun, Hui;  Xiaotao, Fang
Favorite | TC[Scopus]:1 | Submit date：2024/01/25
Distribution Network  Global Sensitivity Analysis  Multi-fidelity Model  Stochastic Source-load  Uncertainty Quantification  
Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm Journal article
Ghiasi, Ramin, Noori, Mohammad, Silik, Ahmed, Wang, Tianyu, Pozo, Francesc, Altabey, Wael A.. Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm[J]. Applied Sciences (Switzerland), 2022, 12(4), 1876.
Authors:  Ghiasi, Ramin;  Noori, Mohammad;  Silik, Ahmed;  Wang, Tianyu;  Pozo, Francesc; et al.
Favorite | TC[WOS]:10 TC[Scopus]:22  IF:2.5/2.7 | Submit date：2022/03/04
Model Updating Method  Non‐probabilistic Structural Damage Identification  Slime Mold Algorithm  Uncertainty Quantification  
Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme Journal article
Yan, Wang Ji, Chronopoulos, Dimitrios, Yuen, Ka Veng, Zhu, Yi Chen. Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme[J]. Mechanical Systems and Signal Processing, 2022, 162(108009).
Authors:  Yan, Wang Ji;  Chronopoulos, Dimitrios;  Yuen, Ka Veng;  Zhu, Yi Chen
Favorite | TC[WOS]:44 TC[Scopus]:46  IF:7.9/8.0 | Submit date：2021/09/10
Transmissibility Function  Bayesian Analysis  Uncertainty Quantification  Probabilistic Distance Measure  Damage Detection  Structural Health Monitoring