| Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model R Cang, H Li, H Yao, Y Jiao, Y Ren Computational Materials Science 150, 212-221, 2018 | 178 | 2018 |
| FEA-Net: A physics-guided data-driven model for efficient mechanical response prediction H Yao, Y Gao, Y Liu Computer Methods in Applied Mechanics and Engineering 363, 112892, 2020 | 83* | 2020 |
| Structural dynamics simulation using a novel physics-guided machine learning method Y Yu, H Yao, Y Liu Engineering Applications of Artificial Intelligence 96, 103947, 2020 | 75 | 2020 |
| One-shot generation of near-optimal topology through theory-driven machine learning R Cang, H Yao, Y Ren Computer-Aided Design 109, 12-21, 2019 | 65 | 2019 |
| A recurrent neural network approach for aircraft trajectory prediction with weather features from sherlock Y Pang, H Yao, J Hu, Y Liu AIAA Aviation 2019 Forum, 3413, 2019 | 55 | 2019 |
| Aircraft dynamics simulation using a novel physics-based learning method Y Yu, H Yao, Y Liu Aerospace Science and Technology 87, 254-264, 2019 | 48 | 2019 |
| A nonlinear dynamic model and parameters identification method for predicting the shock pulse of rubber waveform generator J Wen, C Liu, H Yao, B Wu International Journal of Impact Engineering 120, 1-15, 2018 | 34 | 2018 |
| Physics-based learning for aircraft dynamics simulation Y Yu, H Yao, Y Liu Phm society conference 10 (1), 2018 | 27 | 2018 |
| On fault diagnosis for high-g accelerometers via data-driven models J Wen, H Yao, Z Ji, B Wu, M Xia IEEE Sensors Journal 21 (2), 1359-1368, 2020 | 17 | 2020 |
| Fedlearn-algo: A flexible open-source privacy-preserving machine learning platform B Liu, C Tan, J Wang, T Zeng, H Shan, H Yao, H Huang, P Dai, L Bo, ... arXiv preprint arXiv:2107.04129, 2021 | 11 | 2021 |
| An efficient and robust system for vertically federated random forest H Yao, J Wang, P Dai, L Bo, Y Chen arXiv preprint arXiv:2201.10761, 2022 | 9 | 2022 |
| Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration H Yao, J Wen, Y Ren, B Wu, Z Ji 2019 IEEE International Conference on Acoustics, Speech and Signal …, 2019 | 9 | 2019 |
| A Deep Learning Approach to Recover High-g Shock Signals from the Faulty Accelerometer J Wen, H Yao, B Wu, Y Ren, Z Ji IEEE Sensors Journal, 2019 | 9 | 2019 |
| Fracture pattern prediction with random microstructure using a physics-informed deep neural networks H Wei, H Yao, Y Pang, Y Liu Engineering Fracture Mechanics 268, 108497, 2022 | 8 | 2022 |
| Self-validating high-g accelerometers through data-driven methods J Wen, H Yao, Z Ji, B Wu, F Xu Sensors and Actuators A: Physical 328, 112803, 2021 | 8 | 2021 |
| Physics-based deep learning for probabilistic fracture analysis of composite materials Y Gao, H Yao, H Wei, Y Liu AIAA Scitech 2020 Forum, 1860, 2020 | 8 | 2020 |
| Dynamic analysis and structure optimization on trapezoidal wave generator for eliminating the over deviation of the residual wave in shock test measurement J Wen, H Yao, B Wu, Z Ji, L Wen, M Xu, Y Jin, X Yan Measurement 182, 109665, 2021 | 6 | 2021 |
| Impressionist: a 3D Peekaboo game for crowdsourcing shape saliency H Yao, MY Ren International Design Engineering Technical Conferences and Computers and …, 2016 | 5 | 2016 |
| Improving Model Robustness with Transformation-Invariant Attacks H Yao, Z Wang, G Nie, Y Mazboudi, Y Yang, Y Ren CVPR 2019 Workshop on Adversarial Machine Learning in Real-World Computer …, 2019 | 3 | 2019 |
| Image Decomposition and Classification through a Generative Model H Yao, M Regan, Y Yang, Y Ren The 26th IEEE International Conference on Image Processing (ICIP), 2019 | 1 | 2019 |
