2 citations · 2 across the 3 of their papers we have counts for
3 papers
math.OC2025
Verifying Probabilistic Regions of Attraction with Neural Lyapunov Functions for Stochastic Systems
Yun Su, Hans De Sterck, Jun Liu
Leveraging a stochastic extension of Zubov's equation, we develop a physics-informed neural network (PINN) approach for learning a neural Lyapunov function that captures the larges…
eess.SY2024
Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems
Thanin Quartz, Ruikun Zhou, Hans De Sterck +1
Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning a…
math.OC2018★ 2 cited
Nesterov Acceleration of Alternating Least Squares for Canonical Tensor Decomposition: Momentum Step Size Selection and Restart Mechanisms
Drew Mitchell, Nan Ye, Hans De Sterck
We present Nesterov-type acceleration techniques for Alternating Least Squares (ALS) methods applied to canonical tensor decomposition. While Nesterov acceleration turns gradient d…