| A study on using image-based machine learning methods to develop surrogate models of stamp forming simulations H Zhou, Q Xu, Z Nie, N Li Journal of Manufacturing Science and Engineering 144 (2), 021012, 2022 | 30 | 2022 |
| An improved numerically-stable equivalent static loads (ESLs) algorithm based on energy-scaling ratio for stiffness topology optimization under crash loads YC Bai, HS Zhou, F Lei, HS Lei Structural and Multidisciplinary Optimization 59, 117-130, 2019 | 25 | 2019 |
| Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach HR Attar, H Zhou, A Foster, N Li Journal of Manufacturing Processes 68, 1650-1671, 2021 | 22 | 2021 |
| SuperMeshing: A new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation Q Xu, Z Nie, H Xu, H Zhou, HR Attar, N Li, F Xie, XJ Liu Journal of Applied Mechanics 89 (1), 011002, 2022 | 15 | 2022 |
| Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks HR Attar, H Zhou, N Li IOP Conference Series: Materials Science and Engineering 1157 (1), 012079, 2021 | 13 | 2021 |
| On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations H Zhou, HR Attar, Y Pan, X Li, PRN Childs, N Li NeurIPS 2021 AI for Science Workshop, 2021 | 5 | 2021 |
| Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process H Zhou, N Li arXiv preprint arXiv:2212.05885, 2022 | | 2022 |
