| Symplectic model reduction of Hamiltonian systems L Peng, K Mohseni SIAM Journal on Scientific Computing 38 (1), A1–A27, 2016 | 158 | 2016 |
| Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning KT Carlberg, A Jameson, MJ Kochenderfer, J Morton, L Peng, ... Journal of Computational Physics 395, 105-124, 2019 | 79 | 2019 |
| Dynamic data driven application system for plume estimation using UAVs L Peng, D Lipinski, K Mohseni Journal of Intelligent & Robotic Systems 74 (1–2), 421–436, 2014 | 51 | 2014 |
| Consensus of self-driven agents with avoidance of collisions L Peng, Y Zhao, B Tian, J Zhang, BH Wang, HT Zhang, T Zhou Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 79 (2 …, 2009 | 46 | 2009 |
| Nonlinear model reduction via a locally weighted POD method L Peng, K Mohseni International Journal for Numerical Methods in Engineering 106 (5), 372–396, 2016 | 39 | 2016 |
| Accelerating consensus of self-driven swarm via adaptive speed J Zhang, Y Zhao, B Tian, L Peng, HT Zhang, BH Wang, T Zhou Physica A: Statistical Mechanics and its Applications 388 (7), 1237–1242, 2009 | 33 | 2009 |
| An online manifold learning approach for model reduction of dynamical systems L Peng, K Mohseni SIAM Journal on Numerical Analysis 52 (4), 1928–1952, 2014 | 22 | 2014 |
| A note on a two-sample T test with one variance unknown L Peng, T Tong Statistical Methodology 8 (6), 528–534, 2011 | 21 | 2011 |
| A DDDAS plume monitoring system with reduced Kalman filter L Peng, M Silic, K Mohseni Procedia Computer Science 51, 2533–2542, 2015 | 16 | 2015 |
| Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators Z Bai, L Peng Advanced Modeling and Simulation in Engineering Sciences 8 (1), 28, 2021 | 15 | 2021 |
| Sensor driven feedback for puff estimation using unmanned aerial vehicles L Peng, K Mohseni International Conference on Unmanned Aircraft Systems (ICUAS), 562–569, 2014 | 13 | 2014 |
| Geometric model reduction of forced and dissipative Hamiltonian systems L Peng, K Mohseni 55th IEEE Conference on Decision and Control (CDC), 2016, 7465–7470, 2016 | 10 | 2016 |
| High Resolution Atmospheric Sensing Using UAVs B Hodgkinson, D Lipinski, L Peng, K Mohseni Distributed Autonomous Robotic Systems (DARS) 104, 31–45, 2014 | 9 | 2014 |
| Structure-preserving model reduction of forced Hamiltonian systems L Peng, K Mohseni arXiv preprint arXiv:1603.03514, 2016 | 6 | 2016 |
| Nonlinear Model Reduction for Parameterized Partial Differential Equation L Peng, K Mohseni 43rd AIAA Fluid Dynamics Conference and Exhibit, AIAA paper, 2013-2967, 2013 | 6* | 2013 |
| Cooperative control using data-driven feedback for mobile sensors B Hodgkinson, D Lipinski, L Peng, K Mohseni International Conference on Robotics and Automation (ICRA), 772–777, 2013 | 6 | 2013 |
| An empirical reduced modeling approach for mobile, distributed sensor platform networks IJ Sledge, L Peng, K Mohseni Dynamic Data-Driven Environmental Systems Science (DyDESS), LNCS 8964, 195–204, 2015 | 2 | 2015 |
| A localized symplectic model reduction technique for parameterized Hamiltonian systems L Peng, K Mohseni American Control Conference (ACC), 2015, 5545–5550, 2015 | 1 | 2015 |
| A symplectic technique for model reduction of wave equations L Peng, K Mohseni 53rd Aerospace Sciences Meeting, AIAA Paper, 2015-1809, 2015 | 1 | 2015 |
| Structure-preserving model reduction for marginally stable LTI systems L Peng, K Carlberg arXiv preprint arXiv:1704.04009, 2017 | | 2017 |
Kamran MohseniProfessor of mechanical and aerospace engineering, University of FloridaEmail verificata su ufl.edu