Search Results for author: Yiming Meng

Found 9 papers, 1 papers with code

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

no code implementations15 Mar 2024 Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis.

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

1 code implementation15 Mar 2024 Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stability regions remain challenging due to the curse of dimensionality.

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

no code implementations15 Feb 2024 Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu

We provide a theoretical analysis of both algorithms in terms of convergence of neural approximations towards the true optimal solutions in a general setting.

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification

no code implementations14 Dec 2023 Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions.

Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems

no code implementations8 Mar 2023 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.

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

no code implementations22 May 2022 Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.

Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions

no code implementations6 Apr 2021 Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).

Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specifications

no code implementations9 Sep 2020 Yiming Meng, Yinan Li, Maxwell Fitzsimmons, Jun Liu

While the converse Lyapunov-barrier theorems are not constructive, as with classical converse Lyapunov theorems, we believe that the unified necessary and sufficient conditions with a single Lyapunov-barrier function are of theoretical interest and can hopefully shed some light on computational approaches.

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