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2020 – today
- 2025
[j5]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Physics-informed neural network Lyapunov functions: PDE characterization, learning, and verification. Autom. 175: 112193 (2025)- 2024
- [j4]Ruikun Zhou
, Maxwell Fitzsimmons, Yiming Meng, Jun Liu:
Physics-Informed Extreme Learning Machine Lyapunov Functions. IEEE Control. Syst. Lett. 8: 1763-1768 (2024) - [j3]Yiming Meng, Jun Liu:
Stochastic Lyapunov-Barrier Functions for Robust Probabilistic Reach-Avoid-Stay Specifications. IEEE Trans. Autom. Control. 69(8): 5470-5477 (2024) - [c13]Yiming Meng, Ruikun Zhou, Jun Liu:
Zubov-Koopman Learning of Maximal Lyapunov Functions. ACC 2024: 4020-4025 - [c12]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems. ACC 2024: 4789-4794 - [c11]Yiming Meng, Ruikun Zhou, Melkior Ornik, Jun Liu:
Koopman-Based Learning of Infinitesimal Generators without Operator Logarithm. CDC 2024: 8302-8307 - [c10]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
TOOL LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction. HSCC 2024: 25:1-25:8 - [c9]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Physics-Informed Neural Networks for Stability Analysis and Control with Formal Guarantees. HSCC 2024: 32:1-32:2 - [c8]Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu:
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification. ICML 2024 - [i14]Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu:
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification. CoRR abs/2402.10119 (2024) - [i13]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems. CoRR abs/2403.10007 (2024) - [i12]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction. CoRR abs/2403.10013 (2024) - [i11]Jun Liu, Maxwell Fitzsimmons, Ruikun Zhou, Yiming Meng:
Formally Verified Physics-Informed Neural Control Lyapunov Functions. CoRR abs/2409.20528 (2024) - [i10]Zhexuan Zeng, Ruikun Zhou, Yiming Meng, Jun Liu:
Data-driven optimal control of unknown nonlinear dynamical systems using the Koopman operator. CoRR abs/2412.01085 (2024) - [i9]Ruikun Zhou, Yiming Meng, Zhexuan Zeng, Jun Liu:
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems. CoRR abs/2412.02807 (2024) - 2023
- [j2]Yiming Meng, Navaratnam Sri Namachchivaya, Nicolas Perkowski:
Hopf Bifurcations of Moore-Greitzer PDE Model with Additive Noise. J. Nonlinear Sci. 33(5): 74 (2023) - [c7]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Towards Learning and Verifying Maximal Neural Lyapunov Functions. CDC 2023: 8012-8019 - [i8]Yiming Meng, Jun Liu:
Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems. CoRR abs/2303.05566 (2023) - [i7]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Towards Learning and Verifying Maximal Neural Lyapunov Functions. CoRR abs/2304.07215 (2023) - [i6]Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou:
Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification. CoRR abs/2312.09131 (2023) - [i5]Chuanzheng Wang, Yiming Meng, Jun Liu, Stephen L. Smith:
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations. CoRR abs/2312.12759 (2023) - 2022
- [j1]Yiming Meng, Yinan Li, Maxwell Fitzsimmons, Jun Liu:
Smooth converse Lyapunov-barrier theorems for asymptotic stability with safety constraints and reach-avoid-stay specifications. Autom. 144: 110478 (2022) - [c6]Yiming Meng, Jun Liu:
Sufficient Conditions for Robust Probabilistic Reach-Avoid-Stay Specifications using Stochastic Lyapunov-Barrier Functions. ACC 2022: 2283-2288 - [c5]Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu:
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions. CDC 2022: 5309-5315 - [c4]Yiming Meng, Jun Liu:
Robustly Complete Finite-State Abstractions for Verification of Stochastic Systems. FORMATS 2022: 80-97 - [i4]Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu:
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions. CoRR abs/2205.11513 (2022) - 2021
- [c3]Yiming Meng, Yinan Li, Jun Liu:
Control of Nonlinear Systems with Reach-Avoid-Stay Specifications: A Lyapunov-Barrier Approach with an Application to the Moore-Greizer Model. ACC 2021: 2284-2291 - [c2]Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu:
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions. CDC 2021: 5924-5931 - [c1]Chuanzheng Wang, Yiming Meng, Yinan Li, Stephen L. Smith, Jun Liu:
Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control. ECC 2021: 1459-1464 - [i3]Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu:
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions. CoRR abs/2104.02585 (2021) - 2020
- [i2]Jun Liu, Yiming Meng, Yinan Li, Maxwell Fitzsimmons:
Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specifications. CoRR abs/2009.04432 (2020) - [i1]Chuanzheng Wang, Yinan Li, Yiming Meng, Stephen L. Smith, Jun Liu:
Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control. CoRR abs/2011.10721 (2020)
Coauthor Index
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last updated on 2025-03-11 20:03 CET by the dblp team
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