Tootfinder

Higher-Order Tensor-Based Deferral of Gaussian Splitting for Orbit Uncertainty Propagation
G. Andrew Siciliano, Keith A. LeGrand, Jackson Kulik
https://arxiv.org/abs/2507.01771

Higher-Order Tensor-Based Deferral of Gaussian Splitting for Orbit Uncertainty Propagation
Accurate propagation of orbital uncertainty is essential for a range of applications within space domain awareness. Adaptive Gaussian mixture-based approaches offer tractable nonlinear uncertainty propagation through splitting mixands to increase resolution in areas of stronger nonlinearities, as well as by reducing mixands to prevent unnecessary computational effort. Recent work introduced principled heuristics that incorporate information from the system dynamics and initial uncertainty to de…

Stochastic Neural Control Barrier Functions
Hongchao Zhang, Manan Tayal, Jackson Cox, Pushpak Jagtap, Shishir Kolathaya, Andrew Clark
https://arxiv.org/abs/2506.21697

Stochastic Neural Control Barrier Functions
Control Barrier Functions (CBFs) are utilized to ensure the safety of control systems. CBFs act as safety filters in order to provide safety guarantees without compromising system performance. These safety guarantees rely on the construction of valid CBFs. Due to their complexity, CBFs can be represented by neural networks, known as neural CBFs (NCBFs). Existing works on the verification of the NCBF focus on the synthesis and verification of NCBFs in deterministic settings, leaving the stochast…