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.
Neural contraction metrics with formal guarantees for discrete-time nonlinear dynamical systems.arXiv preprint arXiv:2504.17102, 2025
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Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
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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.