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Number of items: 15.
Journal Article
Li, Rui, Zhang, Jincheng, Zhao, Xiaowei, Wang, Daming, Hann, Martyn and Greaves, Deborah (2023) Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments. Applied Energy, 348 . 121529. doi:10.1016/j.apenergy.2023.121529 ISSN 0306-2619.
Zhang, Jincheng and Zhao, Xiaowei (2023) Digital twin of wind farms via physics-informed deep learning. Energy Conversion and Management, 293 . 117507. doi:10.1016/j.enconman.2023.117507 ISSN 0196-8904.
Li, Rui, Zhang, Jincheng and Zhao, Xiaowei (2022) Dynamic wind farm wake modeling based on a bilateral convolutional neural network and high-fidelity LES data. Energy, 258 . 124845. doi:10.1016/j.energy.2022.124845 ISSN 0360-5442.
Li, Rui, Zhang, Jincheng and Zhao, Xiaowei (2022) Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network. Energy Conversion and Management, 270 . 116185. doi:10.1016/j.enconman.2022.116185
Zhang, Jincheng, Zhao, Xiaowei, Jin, Siya and Greaves, Deborah (2022) Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning. Applied Energy, 324 . 119711. doi:10.1016/j.apenergy.2022.119711 ISSN 0306-2619.
Zhang, Jincheng and Zhao, Xiaowei (2022) Wind farm wake modeling based on deep convolutional conditional generative adversarial network. Energy, 238 (Part B). 121747. doi:10.1016/j.energy.2021.121747 ISSN 0360-5442.
Zhang, Jincheng and Zhao, Xiaowei (2021) Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning. Applied Energy, 300 . 117390. doi:10.1016/j.apenergy.2021.117390 ISSN 0306-2619.
Dong, Hongyang, Zhang, Jincheng and Zhao, Xiaowei (2021) Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations. Applied Energy, 292 . 116928. doi:10.1016/j.apenergy.2021.116928 ISSN 0306-2619.
Zhang, Jincheng and Zhao, Xiaowei (2021) Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements. Applied Energy, 288 . 116641. doi:10.1016/j.apenergy.2021.116641 ISSN 0306-2619.
Zhang, Jincheng and Zhao, Xiaowei (2021) Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures. AIAA Journal, 59 (3). pp. 868-879. doi:10.2514/1.J059877 ISSN 0001-1452.
Zhang, Jincheng, Zhao, Xiaowei and Wei, Xing (2020) Reinforcement learning-based structural control of floating wind turbines. IEEE Transactions on Systems, Man, and Cybernetics: Systems . pp. 1-11. doi:10.1109/TSMC.2020.3032622 ISSN 2168-2216.
Zhang, Jincheng and Zhao, Xiaowei (2020) A novel dynamic wind farm wake model based on deep learning. Applied Energy, 277 . 115552. doi:10.1016/j.apenergy.2020.115552 ISSN 0306-2619.
Zhang, Jincheng and Zhao, Xiaowei (2020) Quantification of parameter uncertainty in wind farm wake modeling. Energy, 196 . 117065. doi:10.1016/j.energy.2020.117065 ISSN 0360-5442.
Conference Item
Li, Rui, Zhang, Jincheng and Zhao, Xiaowei (2022) Deep learning-based wind farm power prediction using Transformer network. In: The 20th European Control Conference, London, England, 12-15 Jul 2022. Published in: 2022 European Control Conference (ECC) ISBN 9781665497336. doi:10.23919/ECC55457.2022.9838022
Zhang, Jincheng, Zhao, Xiaowei and Wei, Xing (2020) Data-driven structural control of monopile wind turbine towers based on machine learning. In: The 21st IFAC World Congress, Berlin, Germany, 12-17 Jul 2020. Published in: IFAC-PapersOnLine, 53 (2). pp. 7466-7471. doi:10.1016/j.ifacol.2020.12.1299 ISSN 2405-8963.
This list was generated on Thu Mar 28 16:20:17 2024 GMT.