A dual-algebraic compiler extracts exact Lie derivatives of neural CBFs via forward passes alone, supporting static C++ deployment on embedded hardware with zero dynamic allocation.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
Combinatorial certificates connect graph topology and control-affine actuation limits to the admissibility of edge-driven diffusive synchronization designs for nonlinear agents.
A saturation-based Optimal Velocity Model is introduced that enforces bounded acceleration, preserves long-wave instability for stop-and-go waves, and modifies the stability threshold compared to the classical OVM.
A structure-preserving linear feedback for ODECO homogeneous polynomial systems yields explicit closed-loop trajectories, convergence thresholds, and sharp ROA characterizations.
citing papers explorer
-
Extracting Exact Lie Derivatives Without Backpropagation: A Dual Compiler for Neural Control Barrier Functions
A dual-algebraic compiler extracts exact Lie derivatives of neural CBFs via forward passes alone, supporting static C++ deployment on embedded hardware with zero dynamic allocation.
-
Combinatorial Admissibility in Control-Affine Networks
Combinatorial certificates connect graph topology and control-affine actuation limits to the admissibility of edge-driven diffusive synchronization designs for nonlinear agents.
-
A Saturation-Based Optimal Velocity Model for Traffic Flow Dynamics
A saturation-based Optimal Velocity Model is introduced that enforces bounded acceleration, preserves long-wave instability for stop-and-go waves, and modifies the stability threshold compared to the classical OVM.
-
Linear Feedback Controller for Homogeneous Polynomial Systems
A structure-preserving linear feedback for ODECO homogeneous polynomial systems yields explicit closed-loop trajectories, convergence thresholds, and sharp ROA characterizations.