4 citations · 4 across the 13 of their papers we have counts for
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Safe PDE Boundary Control with Neural Operators
Hanjiang Hu, Changliu Liu
The physical world dynamics are generally governed by underlying partial differential equations (PDEs) with unknown analytical forms in science and engineering problems. Neural net…
Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation
Hanjiang Hu, Yujie Yang, Tianhao Wei +1
Control barrier functions (CBFs) are important in safety-critical systems and robot control applications. Neural networks have been used to parameterize and synthesize CBFs with bo…
Robust Tracking Control with Neural Network Dynamic Models under Input Perturbations
Huixuan Cheng, Hanjiang Hu, Changliu Liu
Robust control problems have significant practical implications since external disturbances can significantly impact the performance of control methods. Existing robust control met…
Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis
Yujie Yang, Hanjiang Hu, Tianhao Wei +2
Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-ter…
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Hanjiang Hu, Luca Marzari +4
Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-outp…
Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation
Hanjiang Hu, Jianglin Lan, Changliu Liu
Safe control of neural network dynamic models (NNDMs) is important to robotics and many applications. However, it remains challenging to compute an optimal safe control in real tim…