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卢文喜
Lu Wenxi
(教授)
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学位：博士
性别：男
学历：博士研究生毕业
在职信息：在职
所在单位：新能源与环境学院
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论文发表
Xu Yaning; Lu Wenxi*; Zhu Liuzhi, Yang Qingchun. Identification of time-varying contaminant discharge rates using a data-driven Transformer model [J]. Journal of Contaminant Hydrology, 2026, 277: 104847. (SCI收录). Journal of Contaminant Hydrology,277
Zhu Liuzhi, Lu Wenxi*. A quantum-inspired attention integrated scalar long short-term memory model for accurate and stable groundwater contaminant source inversion [J]. Environmental Monitoring and Assessment, 2026, 198 (2): 124. (SCI收录). Environmental Monitoring and Assessment. 2026,198 (2)
Xu Yaning; Lu Wenxi*; Pan Zidong; Wang Zibo. Framework for Identification of Groundwater Contamination Source Based on Conditional Generative Adversarial Networks and Optimization Methods [J]. Water Resources Research, 2025, 61(10): e2024WR039467. (SCI收录). WATER RESOURCES RESEARCH. 2025,61 (10)
Zhu Liuzhi; Lu Wenxi*; Luo Chengming. A high-precision and interpretability-enhanced direct inversion framework for groundwater contaminant source identification using multiple machine learning techniques [J]. Journal of Hydrology, 2025, 659: 133237. (SCI收录). JOURNAL OF HYDROLOGY. 2025,659
Wang Xiao; Lu Wenxi*; Wang Zibo. Simultaneous identification of groundwater contamination source and simulation model parameters based on the rime optimization algorithm [J]. Environmental Monitoring and Assessment, 2025, 197(9): 978. (SCI收录). Environmental Monitoring and Assessment. 2025,197 (9)
Pan Zidong; Guo Zhilin*; Chen Kewei; Lu Wenxi*; Zheng Chunmiao. A deep adaptive bidirectional generative adversarial neural network (Bi-GAN) for groundwater contamination source estimation [J]. Journal of Hydrology, 2025, 653: 132753. (SCI收录). Journal of Hydrology. 2025,653
Tao Zhang; Lu Wenxi*; Pan Zidong; Ge Yuanbo; Luo Chengming; Wang Zibo. Groundwater contamination source identification based on Rat Swarm Optimizer algorithm with a back-propagation neural network surrogate model [J]. Water Supply, 2024, 24(10): 3379–3397. (EI收录). Water Supply. 2024,24 (10):3379-3397
Wang Zibo; Lu Wenxi*; Chang Zhenbo; Zhang Tao. Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy [J]. Journal of Hydrology,2024, 636:131320. (SCI收录)
Xu Yaning; Lu Wenxi*; Pan Zidong; Wang Zibo; Luo Chengming; Bai Yukun. Intelligent enhanced particle filter with deep residual network surrogate for accurate groundwater pollution source characterization [J]. Journal of Hydrology,2024, 642:131904. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo. Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition [J]. Science of the Total Environment,2024, 946:174374. (SCI收录)
Xu Yaning; Lu Wenxi*; Pan Zidong; Luo Chengming; Bai Yukun; Qiu Shuwei. Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate [J]. Geoscience Frontiers,2024, 15(1):101732. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo. Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter [J]. Journal of Hydrology,2023, 625: 129965. (SCI收录)
Pan Zidong; Lu Wenxi*; Bai Yukun. Groundwater contaminated source estimation based on adaptive correction iterative ensemble smoother with an auto lightgbm surrogate [J]. Journal of Hydrology, 2023, 620: 129502. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo; Luo Jiannan. A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification [J]. Journal of Hydrology, 2023, 616: 128854. (SCI收录)
Pan Zidong; Lu Wenxi*; Wang Han; Bai Yukun. Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate [J]. Environmental Modelling & Software, 2023, 159: 105588. (SCI收录)
Pan Zidong; Lu Wenxi*; Wang Han; Bai Yukun. Fast inverse estimation of hydraulic conductivity field based on a deep convolutional-cycle generative adversarial neural network [J]. Journal of Hydrology, 2022, 613: 128420. (SCI收录)
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