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 Kolathaya AI4CE 25 0 0 15 Apr 2025
Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems Ruikun Zhou Yiming Meng Zhexuan Zeng Jun Liu 80 0 0 03 Dec 2024
Formally Verified Physics-Informed Neural Control Lyapunov Functions Jun Liu Maxwell Fitzsimmons Ruikun Zhou Yiming Meng 29 2 0 30 Sep 2024
Learning and Verifying Maximal Taylor-Neural Lyapunov functions Matthieu Barreau Nicola Bastianello 31 1 0 30 Aug 2024
Deep Learning for Computing Convergence Rates of Markov Chains Yanlin Qu Jose H. Blanchet Peter Glynn BDL 21 0 0 30 May 2024
Manifold-Guided Lyapunov Control with Diffusion Models Amartya Mukherjee Thanin Quartz Jun Liu 44 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 32 16 0 15 Mar 2024
Actor-Critic Physics-informed Neural Lyapunov Control Jiarui Wang Mahyar Fazlyab 30 4 0 13 Mar 2024
Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation Nathan Gaby Fumin Zhang X. Ye PINN 40 39 0 27 Sep 2021