| Machine learning for structural health monitoring: challenges and opportunities FG Yuan, SA Zargar, Q Chen, S Wang Sensors and smart structures technologies for civil, mechanical, and …, 2020 | 181 | 2020 |
| Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods Q Chen, J Wang, P Pope, W Chen, M Fuge Journal of Mechanical Design 144 (2), 021712, 2022 | 29 | 2022 |
| Learning airfoil manifolds with optimal transport Q Chen, P Pope, M Fuge AIAA SCITECH 2022 Forum, 2352, 2022 | 7 | 2022 |
| Compressing Latent Space via Least Volume Q Chen, M Fuge arXiv preprint arXiv:2404.17773, 2024 | 1 | 2024 |
| Physics Informed Learning for Dynamic Modeling of Beam Structures Q Chen | 1 | 2020 |
| Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces Q Chen, P Tsilifis, M Fuge arXiv preprint arXiv:2405.14008, 2024 | | 2024 |
| Characterizing Designs via Isometric Embeddings: Applications to Airfoil Inverse Design Q Chen, M Fuge Journal of Mechanical Design 146 (5), 051702, 2024 | | 2024 |
| An Epsilon-Frontier for Faster Optimization in Nonlinear Manifold Learning A Drake, Q Chen, M Fuge AAAI 2022 Workshop on AI for Design and Manufacturing (ADAM), 2022 | | 2022 |
| Physics-informed Artificial Neural Networks for Dynamic Modeling of Structures Q Chen North Carolina State University, 2020 | | 2020 |
