3 citations · 3 across the 7 of their papers we have counts for
7 papers
Demystifying Lipschitz verification: positive matrices, negative results
Simon Kuang, Yuezhu Xu, S. Sivaranjani +1
The global Lipschitz constant of a neural network is related to robustness and generalization, yet unlike in many classical models, it is not plainly legible from the parameters. T…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
Learning Neural Network Safe Tracking Controllers from Backward Reachable Sets
Yuezhu Xu, Mohamed Serry, Jun Liu +1
The design of tracking controllers that closely follow a reference trajectory while ensuring safety and robustness against disturbances is a challenging problem in the control of a…
ECLipsE-Gen-Local: Efficient Compositional Local Lipschitz Estimates for Deep Neural Networks
Yuezhu Xu, S. Sivaranjani
The Lipschitz constant is a key measure for certifying the robustness of neural networks to input perturbations. However, computing the exact constant is NP-hard, and standard appr…
Learning Neural Koopman Operators with Dissipativity Guarantees
Yuezhu Xu, S. Sivaranjani, Vijay Gupta
We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative.…
ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
Yuezhu Xu, S. Sivaranjani
The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations. Since calculating the exact Lipschitz constant is NP-hard, effor…
Learning Dissipative Neural Dynamical Systems
Yuezhu Xu, S. Sivaranjani
Consider an unknown nonlinear dynamical system that is known to be dissipative. The objective of this paper is to learn a neural dynamical model that approximates this system, whil…