A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
Lyapunov-stable neural control for state and output feedback: A novel formulation for efficient synthesis and verification
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
citing papers explorer
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Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
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Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.
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Learned Lyapunov Shielding for Adaptive Control
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.
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Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
- Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions