| Machine Learning in Aerodynamic Shape Optimization J Li, X Du, JRRA Martins Progress in Aerospace Sciences 134, 100849, 2022 | 231 | 2022 |
| Robust aerodynamic shape optimization—from a circle to an airfoil X He, J Li, CA Mader, A Yildirim, JRRA Martins Aerospace Science and Technology 87, 48-61, 2019 | 160 | 2019 |
| Efficient aerodynamic shape optimization with deep-learning-based geometric filtering J Li, M Zhang, JRRA Martins, C Shu AIAA journal 58 (10), 4243-4259, 2020 | 157 | 2020 |
| Data-based approach for fast airfoil analysis and optimization J Li, MA Bouhlel, JRRA Martins AIAA Journal 57 (2), 581-596, 2019 | 140 | 2019 |
| Reinforcement-learning-based control of confined cylinder wakes with stability analyses J Li, M Zhang Journal of Fluid Mechanics 932, A44, 2022 | 89 | 2022 |
| Surrogate-based aerodynamic shape optimization with the active subspace method J Li, J Cai, K Qu Structural and Multidisciplinary Optimization, 1-17, 2018 | 72 | 2018 |
| On deep-learning-based geometric filtering in aerodynamic shape optimization J Li, M Zhang Aerospace Science and Technology 112, 106603, 2021 | 68 | 2021 |
| Data-based approach for wing shape design optimization J Li, M Zhang Aerospace Science and Technology 112, 106639, 2021 | 66 | 2021 |
| Low-Reynolds-number airfoil design optimization using deep-learning-based tailored airfoil modes J Li, M Zhang, CMJ Tay, N Liu, Y Cui, SC Chew, BC Khoo Aerospace Science and Technology 121, 107309, 2022 | 56 | 2022 |
| Adjoint-free aerodynamic shape optimization of the common research model wing J Li, M Zhang AIAA Journal 59 (6), 1990-2000, 2021 | 42 | 2021 |
| Data-driven constraint approach to ensure low-speed performance in transonic aerodynamic shape optimization J Li, S He, JRRA Martins Aerospace Science and Technology 92, 536-550, 2019 | 33 | 2019 |
| Massively multipoint aerodynamic shape design via surrogate-assisted gradient-based optimization J Li, J Cai AIAA Journal 58 (5), 1949-1963, 2020 | 29 | 2020 |
| Adjoint-based two-step optimization method using proper orthogonal decomposition and domain decomposition J Li, J Cai, K Qu AIAA Journal 56 (3), 1133-1145, 2018 | 29 | 2018 |
| Physics-based data-driven buffet-onset constraint for aerodynamic shape optimization J Li, S He, M Zhang, JRRA Martins, B Cheong Khoo AIAA Journal 60 (8), 4775-4788, 2022 | 23 | 2022 |
| Drag reduction of transonic wings with surrogate-based optimization J Li, J Cai, K Qu The Proceedings of the 2018 Asia-Pacific International Symposium on …, 2019 | 13 | 2019 |
| Efficient data-driven off-design constraint modeling for practical aerodynamic shape optimization J Li, S He, JRRA Martins, M Zhang, B Cheong Khoo AIAA Journal 61 (7), 2854-2866, 2023 | 9 | 2023 |
| Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades J Li, MH Dao, QT Le Renewable Energy 224, 120115, 2024 | 7 | 2024 |
| An efficient multistep ROM method for prediction of flows over airfoils C Cao, J Cai, K Qu, J Li 55th AIAA aerospace sciences meeting, 1421, 2017 | 7 | 2017 |
| Adjoint approach based on reduced-order model for steady PDE systems J Li, K Qu, J Cai, C Cao 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 3668, 2016 | 4 | 2016 |
| Multi-fidelity Data-driven Aerodynamic Shape Optimization of Wings with Composite Neural Networks A Yang, J Li, RP Liem AIAA AVIATION 2023 Forum, 3470, 2023 | 2 | 2023 |
Joaquim R. R. A. MartinsPauline M. Sherman Collegiate Professor of Aerospace Engineering, University of MichiganDirección de correo verificada de umich.edu
