HardNet++ Enforces Nonlinear Constraints in Neural Network Outputs for Safety-Critical Applications

arXiv·medium signal

Goertzen, Alim, and Azizan present HardNet++, which guarantees constraint satisfaction in neural network outputs through architectural enforcement rather than soft penalty terms. Unlike soft-constrained methods that penalize violations during training (no guarantees at inference), HardNet++ provides hard guarantees for nonlinear constraints. Critical for safety-critical deployment in control, robotics, and decision-making where constraint violation has real consequences.