| An agent-based model for energy investment decisions in the residential sector J Sachs, Y Meng, S Giarola, A Hawkes Energy 172, 752-768, 2019 | 73 | 2019 |
| Hydroconversion behavior of asphaltenes under liquid-phase hydrogenation conditions N Jin, G Wang, S Han, Y Meng, C Xu, J Gao Energy & Fuels 30 (4), 2594-2603, 2016 | 30 | 2016 |
| Safety-critical control of stochastic systems using stochastic control barrier functions C Wang, Y Meng, SL Smith, J Liu 2021 60th IEEE Conference on Decision and Control (CDC), 5924-5931, 2021 | 29 | 2021 |
| Learning control barrier functions with high relative degree for safety-critical control C Wang, Y Meng, Y Li, SL Smith, J Liu 2021 European Control Conference (ECC), 1459-1464, 2021 | 27 | 2021 |
| Smooth converse Lyapunov-barrier theorems for asymptotic stability with safety constraints and reach-avoid-stay specifications Y Meng, Y Li, M Fitzsimmons, J Liu Automatica 144, 110478, 2022 | 21 | 2022 |
| Physics-informed neural network Lyapunov functions: PDE characterization, learning, and verification J Liu, Y Meng, M Fitzsimmons, R Zhou arXiv preprint arXiv:2312.09131, 2023 | 14 | 2023 |
| Control of nonlinear systems with reach-avoid-stay specifications: A Lyapunov-barrier approach with an application to the moore-greizer model Y Meng, Y Li, J Liu 2021 American Control Conference (ACC), 2284-2291, 2021 | 13 | 2021 |
| Towards learning and verifying maximal neural lyapunov functions J Liu, Y Meng, M Fitzsimmons, R Zhou 2023 62nd IEEE Conference on Decision and Control (CDC), 8012-8019, 2023 | 9 | 2023 |
| Sufficient conditions for robust probabilistic reach-avoid-stay specifications using stochastic lyapunov-barrier functions Y Meng, J Liu 2022 American Control Conference (ACC), 2283-2288, 2022 | 9 | 2022 |
| TOOL LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction J Liu, Y Meng, M Fitzsimmons, R Zhou Proceedings of the 27th ACM International Conference on Hybrid Systems …, 2024 | 8 | 2024 |
| Learning regions of attraction in unknown dynamical systems via zubov-koopman lifting: Regularities and convergence Y Meng, R Zhou, J Liu arXiv preprint arXiv:2311.15119, 2023 | 8 | 2023 |
| Physics-informed neural network policy iteration: Algorithms, convergence, and verification Y Meng, R Zhou, A Mukherjee, M Fitzsimmons, C Song, J Liu Forty-first International Conference on Machine Learning, 2024 | 7 | 2024 |
| Lyapunov-barrier characterization of robust reach–avoid–stay specifications for hybrid systems Y Meng, J Liu Nonlinear Analysis: Hybrid Systems 49, 101340, 2023 | 7 | 2023 |
| Data-driven learning of safety-critical control with stochastic control barrier functions C Wang, Y Meng, SL Smith, J Liu 2022 IEEE 61st Conference on Decision and Control (CDC), 5309-5315, 2022 | 5 | 2022 |
| Bifurcation and robust control of instabilities in the presence of uncertainties Y Meng University of Waterloo, 2022 | 3 | 2022 |
| Koopman-Based Learning of Infinitesimal Generators without Operator Logarithm Y Meng, R Zhou, M Ornik, J Liu arXiv preprint arXiv:2403.15688, 2024 | 2 | 2024 |
| Compositionally verifiable vector neural Lyapunov functions for stability analysis of interconnected nonlinear systems J Liu, Y Meng, M Fitzsimmons, R Zhou arXiv preprint arXiv:2403.10007, 2024 | 2 | 2024 |
| Hopf bifurcations of moore-greitzer pde model with additive noise Y Meng, NS Namachchivaya, N Perkowski Journal of Nonlinear Science 33 (5), 74, 2023 | 2 | 2023 |
| Robustly complete finite-state abstractions for verification of stochastic systems Y Meng, J Liu International Conference on Formal Modeling and Analysis of Timed Systems, 80-97, 2022 | 2 | 2022 |
| Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems Y Meng, J Liu IEEE Open Journal of Control Systems 2, 235 - 248, 2023 | 1 | 2023 |
Jun LiuProfessor of Applied Mathematics, University of WaterlooVerified email at uwaterloo.ca
