| Ranking and selection as stochastic control Y Peng, EKP Chong, CH Chen, MC Fu IEEE Transactions on Automatic Control 63 (8), 2359-2373, 2018 | 105 | 2018 |
| A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters Y Peng, MC Fu, JQ Hu, B Heidergott Operations Research 66 (2), 487-499, 2018 | 86 | 2018 |
| Dynamic sampling allocation and design selection Y Peng, CH Chen, MC Fu, JQ Hu INFORMS Journal on Computing 28 (2), 195-208, 2016 | 68 | 2016 |
| Validation of digital twins: challenges and opportunities EY Hua, S Lazarova-Molnar, DP Francis 2022 Winter Simulation Conference (WSC), 2900-2911, 2022 | 66 | 2022 |
| Multi-agent deep reinforcement learning for multi-echelon inventory management X Liu, M Hu, Y Peng, Y Yang Production and Operations Management 34 (7), 1836-1856, 2025 | 50 | 2025 |
| Myopic allocation policy with asymptotically optimal sampling rate Y Peng, MC Fu IEEE Transactions on Automatic Control 62 (4), 2041-2047, 2016 | 48 | 2016 |
| Noise optimization in artificial neural networks L Xiao, Z Zhang, K Huang, J Jiang, Y Peng IEEE Transactions on Automation Science and Engineering 22, 2780-2793, 2024 | 40 | 2024 |
| Maximum likelihood estimation by Monte Carlo simulation: Toward data-driven stochastic modeling Y Peng, MC Fu, B Heidergott, H Lam Operations Research 68 (6), 1896-1912, 2020 | 38 | 2020 |
| Online validation of simulation-based digital twins exploiting time series analysis G Lugaresi, S Gangemi, G Gazzoni, A Matta 2022 Winter Simulation Conference (WSC), 2912-2923, 2022 | 35 | 2022 |
| Efficient simulation sampling allocation using multifidelity models Y Peng, J Xu, LH Lee, J Hu, CH Chen IEEE Transactions on Automatic Control 64 (8), 3156-3169, 2018 | 34 | 2018 |
| Efficient simulation resource sharing and allocation for selecting the best Y Peng, CH Chen, MC Fu, JQ Hu IEEE Transactions on Automatic Control 58 (4), 1017-1023, 2012 | 33 | 2012 |
| Efficient learning for clustering and optimizing context-dependent designs H Li, H Lam, Y Peng Operations Research 72 (2), 617-638, 2024 | 29 | 2024 |
| A new likelihood ratio method for training artificial neural networks Y Peng, L Xiao, B Heidergott, LJ Hong, H Lam INFORMS Journal on Computing 34 (1), 638-655, 2022 | 29 | 2022 |
| Gradient-based myopic allocation policy: An efficient sampling procedure in a low-confidence scenario Y Peng, CH Chen, MC Fu, JQ Hu IEEE Transactions on Automatic Control 63 (9), 3091-3097, 2017 | 28 | 2017 |
| A stochastic approximation method for simulation-based quantile optimization J Hu, Y Peng, G Zhang, Q Zhang INFORMS Journal on Computing 34 (6), 2889-2907, 2022 | 27 | 2022 |
| Computing sensitivities for distortion risk measures PW Glynn, Y Peng, MC Fu, JQ Hu INFORMS Journal on Computing 33 (4), 1520-1532, 2021 | 27 | 2021 |
| On the variance of single-run unbiased stochastic derivative estimators Z Cui, MC Fu, JQ Hu, Y Liu, Y Peng, L Zhu INFORMS Journal on Computing 32 (2), 390-407, 2020 | 26 | 2020 |
| Context-dependent ranking and selection under a bayesian framework H Li, H Lam, Z Liang, Y Peng 2020 winter simulation conference (WSC), 2060-2070, 2020 | 24 | 2020 |
| Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation L Lei, Y Peng, MC Fu, JQ Hu Discrete Event Dynamic Systems 28 (1), 109-125, 2018 | 24 | 2018 |
| On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation Y Peng, MC Fu, PW Glynn, J Hu 2017 Winter Simulation Conference (WSC), 2336-2347, 2017 | 23 | 2017 |
Michael C. FuUniversity of MarylandVerified email at umd.edu
