| Supervised learning mixing characteristics of film cooling in a rocket combustor using convolutional neural networks H Ma, Y Zhang, OJ Haidn, N Thuerey, X Hu Acta Astronautica 175, 11-18, 2020 | 40 | 2020 |
| Physics-driven learning of the steady Navier-Stokes equations using deep convolutional neural networks H Ma, Y Zhang, N Thuerey, X Hu, OJ Haidn Communications in Computational Physics 32 (3), 715-736, 2022 | 37 | 2022 |
| A combined data-driven and physics-driven method for steady heat conduction prediction using deep convolutional neural networks H Ma, X Hu, Y Zhang, N Thuerey, OJ Haidn arXiv preprint arXiv:2005.08119, 2020 | 24 | 2020 |
| A viscoelastic constitutive model of composite propellant considering dewetting and strain‐rate and its implementation H Ma, ZB Shen, DK Li Propellants, Explosives, Pyrotechnics 44 (6), 759-768, 2019 | 18 | 2019 |
| Turbulence and combustion and film prediction in rocket application via parameter adjustion, model variation and deep learning method A Sternin, H Ma, J Liu, O Haidn, M Tajmar Transregio 40-Summer Program Report, 2019 | 7 | 2019 |
| Generative adversarial networks with physical evaluators for spray simulation of pintle injector H Ma, B Zhang, C Zhang, OJ Haidn AIP Advances 11 (7), 2021 | 6 | 2021 |
| A comprehensive deep learning geometric shape optimization framework with field prediction surrogate and reinforcement learning H Ma, J Liu, M Ye, OJ Haidn Physics of Fluids 36 (4), 2024 | 4 | 2024 |
| Heat conduction plate layout optimization using physics-driven convolutional neural networks Y Sun, A Elhanashi, H Ma, MR Chiarelli Applied Sciences 12 (21), 10986, 2022 | 4 | 2022 |
| Neural image beauty predictor based on bradley-terry model S Li, H Ma, X Hu arXiv preprint arXiv:2111.10127, 2021 | 2 | 2021 |
| DRLinSPH: An open-source platform using deep reinforcement learning and SPHinXsys for fluid-structure-interaction problems M Ye, H Ma, Y Ren, C Zhang, OJ Haidn, X Hu arXiv preprint arXiv:2409.20134, 2024 | | 2024 |
| 3.2 A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolu-tional Neural Networks H Ma Physics field prediction using convolutional neural networks, 40, 2022 | | 2022 |
| Summaries of publications H Ma Physics field prediction using convolutional neural networks, 37, 2022 | | 2022 |
| A. 3 Paper III H Ma, YX Zhang, N Thuerey, XY Hu, OJ Haidn Physics field prediction using convolutional neural networks, 2022 | | 2022 |
| Physics field prediction using convolutional neural networks H Ma Technische Universität München, 2022 | | 2022 |
| 机匣螺栓连接结构分析及参数优化程序开发 马浩， 张亚辉， 朴思扬 强度与环境 44 (3), 25-30, 2017 | | 2017 |
| HTPB 推进剂 “脱湿点” 及快慢组合拉伸研究 马浩， 职世君， 申志彬， 李道奎 固体火箭技术 40 (6), 741-745, 2017 | | 2017 |
Xiangyu Hu (胡湘渝)Techincal University of Munich在 tum.de 的电子邮件经过验证
