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Wenchong Tian

Showing results (11-20 of 18) with videos related to

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Environmental Science and Pollution Research International|January 10, 2019
CBR-based integration of a hydrodynamic and water quality model and GIS-a case study of Chaohu CityZhenliang Liao, Can Zhou, Wenchong Tian, et al.
Water Research|July 3, 2026
Physics-informed graph inference and prediction for global state estimation in water distribution networksYacan Man, Xiao Zhou, Wenchong Tian, et al.
Water Research|June 15, 2025
Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospectsZichen He, Wenchong Tian, Jiaying Wang, et al.
Water Research X|November 5, 2024
Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learningShixun Li, Wenchong Tian, Hexiang Yan, et al.
Water Research|June 1, 2026
Making waves: Model transfer as a key pathway to improving the generalization of machine learning in wastewater treatment engineeringYu-Qi Wang, Wenchong Tian, Hong-Cheng Wang, et al.
Water Research|December 16, 2023
Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake ZurichWenchong Tian, Zhiyu Zhang, Damien Bouffard, et al.
Water Research|November 9, 2025
A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systemsWenchong Tian, Zhiyu Zhang, Xuan Wang, et al.
Journal of Environmental Management|May 26, 2026
Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learningWeihao Chen, Wenchong Tian, Chao Lu, et al.
Pageof 2

Showing results (11-20 of 18) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 18 results.
Environmental Science and Pollution Research International|January 10, 2019
CBR-based integration of a hydrodynamic and water quality model and GIS-a case study of Chaohu CityZhenliang Liao, Can Zhou, Wenchong Tian, et al.
Water Research|July 3, 2026
Physics-informed graph inference and prediction for global state estimation in water distribution networksYacan Man, Xiao Zhou, Wenchong Tian, et al.
Water Research|June 15, 2025
Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospectsZichen He, Wenchong Tian, Jiaying Wang, et al.
Water Research X|November 5, 2024
Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learningShixun Li, Wenchong Tian, Hexiang Yan, et al.
Water Research|June 1, 2026
Making waves: Model transfer as a key pathway to improving the generalization of machine learning in wastewater treatment engineeringYu-Qi Wang, Wenchong Tian, Hong-Cheng Wang, et al.
Water Research|December 16, 2023
Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake ZurichWenchong Tian, Zhiyu Zhang, Damien Bouffard, et al.
Water Research|November 9, 2025
A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systemsWenchong Tian, Zhiyu Zhang, Xuan Wang, et al.
Journal of Environmental Management|May 26, 2026
Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learningWeihao Chen, Wenchong Tian, Chao Lu, et al.
Pageof 2