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

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

14 December 2023

Jun Liu

Maxwell Fitzsimmons

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

9 / 9 papers shown

Title
Neural Control Barrier Functions from Physics Informed Neural Networks Shreenabh Agrawal Manan Tayal Aditya Singh Shishir N Y Kolathaya AI4CE 20 0 0 15 Apr 2025
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems Ruikun Zhou Yiming Meng Zhexuan Zeng Jun Liu 62 0 0 03 Dec 2024
Formally Verified Physics-Informed Neural Control Lyapunov Functions Jun Liu Maxwell Fitzsimmons Ruikun Zhou Yiming Meng 19 1 0 30 Sep 2024
Learning and Verifying Maximal Taylor-Neural Lyapunov functions Matthieu Barreau Nicola Bastianello 16 0 0 30 Aug 2024
Deep Learning for Computing Convergence Rates of Markov Chains Yanlin Qu Jose H. Blanchet Peter Glynn BDL 16 0 0 30 May 2024
Manifold-Guided Lyapunov Control with Diffusion Models Amartya Mukherjee Thanin Quartz Jun Liu 28 0 0 26 Mar 2024
LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction Jun Liu Yiming Meng Maxwell Fitzsimmons Rui Zhou 22 12 0 15 Mar 2024
Actor-Critic Physics-informed Neural Lyapunov Control Jiarui Wang Mahyar Fazlyab 22 1 0 13 Mar 2024
Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation Nathan Gaby Fumin Zhang X. Ye PINN 27 38 0 27 Sep 2021