Paper-Conference

Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification

Y. Meng, R. Zhou, A. Mukherjee, M. Fitzsimmons, C. Song, J. Liu

Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems

J. Liu, Y. Meng, M. Fitzsimmons, R. Zhou

Zubov-Koopman Learning of Maximal Lyapunov Functions

Y. Meng, R. Zhou, J. Liu

LyZNet with Control: Physics-Informed Neural Network Control of Nonlinear Systems with Formal Guarantees

J. Liu, Y. Meng, R. Zhou

LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction

J. Liu, Y. Meng, M. Fitzsimmons, R. Zhou

Towards Learning and Verifying Maximal Neural Lyapunov Functions

J. Liu, Y. Meng, M. Fitzsimmons, R. Zhou

Asymptotic Approximation of the Maximal Lyapunov Exponent of Moore-Greitzer PDE model with Multiplicative Noise close to Stall Bifurcation

Y. Meng, N.S. Namachchivaya, N. Perkowski

Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions

Equal Contribution

C. Wang, Y. Meng, S.L. Smith, J. Liu

Robustly Complete Finite-State Abstractions for Verification of Stochastic Systems

Y. Meng, J. Liu

Sufficient Conditions for Robust Probabilistic Reach-Avoid-Stay Specifications using Stochastic Lyapunov-Barrier Functions

Y. Meng, J. Liu