| Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations H Dong, J Zhang, X Zhao Applied Energy 292, 116928, 2021 | 63 | 2021 |
| Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements J Zhang, X Zhao Applied Energy 288, 116641, 2021 | 57 | 2021 |
| A novel dynamic wind farm wake model based on deep learning J Zhang, X Zhao Applied Energy 277, 115552, 2020 | 53 | 2020 |
| Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning J Zhang, X Zhao Applied Energy 300, 117390, 2021 | 44 | 2021 |
| An efficient approach for quantifying parameter uncertainty in the SST turbulence model J Zhang, S Fu Computers & Fluids 181, 173-187, 2019 | 39 | 2019 |
| An efficient Bayesian uncertainty quantification approach with application to k-ω-γ transition modeling J Zhang, S Fu Computers & Fluids 161, 211-224, 2018 | 36 | 2018 |
| Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning J Zhang, X Zhao, S Jin, D Greaves Applied Energy 324, 119711, 2022 | 34 | 2022 |
| Wind farm wake modeling based on deep convolutional conditional generative adversarial network J Zhang, X Zhao Energy 238, 121747, 2022 | 30 | 2022 |
| Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R Li, J Zhang, X Zhao Energy 258, 124845, 2022 | 29 | 2022 |
| Quantification of parameter uncertainty in wind farm wake modeling J Zhang, X Zhao Energy 196, 117065, 2020 | 22 | 2020 |
| Reinforcement learning-based structural control of floating wind turbines J Zhang, X Zhao, X Wei IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (3), 1603-1613, 2020 | 20 | 2020 |
| Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures J Zhang, X Zhao AIAA Journal 59 (3), 868-879, 2021 | 16 | 2021 |
| Digital twin of wind farms via physics-informed deep learning J Zhang, X Zhao Energy Conversion and Management 293, 117507, 2023 | 13 | 2023 |
| Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network R Li, J Zhang, X Zhao Energy Conversion and Management 270, 116185, 2022 | 9 | 2022 |
| Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments J Zhang, X Zhao, D Greaves, S Jin Applied Energy 341, 121072, 2023 | 7 | 2023 |
| Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments R Li, J Zhang, X Zhao, D Wang, M Hann, D Greaves Applied Energy 348, 121529, 2023 | 3 | 2023 |
| Deep learning-based wind farm power prediction using Transformer network R Li, J Zhang, X Zhao 2022 European Control Conference (ECC), 1018-1023, 2022 | 3 | 2022 |
| Long-distance and high-impact wind farm wake effects revealed by SAR: a global-scale study R Li, J Zhang, X Zhao arXiv preprint arXiv:2311.18124, 2023 | 1 | 2023 |
| Data-driven Structural Control of Monopile Wind Turbine Towers Based on Machine Learning⋆ J Zhang, X Zhao, X Wei | 1 | 2020 |
| Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks J Zhang, X Zhao Journal of Physics: Conference Series 2767 (9), 092017, 2024 | | 2024 |
