28 citations · 70 across the 25 of their papers we have counts for
5 papers · 1 filter
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations
Chuanzheng Wang, Yiming Meng, Jun Liu +1
Control barrier functions are widely used to synthesize safety-critical controls. However, the presence of Gaussian-type noise in dynamical systems can generate unbounded signals a…
Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE…
Learning Regions of Attraction in Unknown Dynamical Systems via Zubov-Koopman Lifting: Regularities and Convergence
Yiming Meng, Ruikun Zhou, Jun Liu
The estimation for the region of attraction (ROA) of an asymptotically stable equilibrium point is crucial in the analysis of nonlinear systems. There has been a recent surge of in…
Towards Learning and Verifying Maximal Neural Lyapunov Functions
Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1
The search for Lyapunov functions is a crucial task in the analysis of nonlinear systems. In this paper, we present a physics-informed neural network (PINN) approach to learning a…
Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems
Yiming Meng, Jun Liu
The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems. Th…