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We present an implementation of interval analysis and mixed monotone intervalreachability analysis as function transforms in Python, fully composable withthe computational framework JAX. The resulting toolbox inherits several keyfeatures from JAX, including computational efficiency through Just-In-TimeCompilation, GPU acceleration for quick parallelized computations, andAutomatic Differentiability. We demonstrate the toolbox's performance onseveral case studies, including a reachability problem on a vehicle modelcontrolled by a neural network, and a robust closed-loop optimal controlproblem for a swinging pendulum.

$\texttt{immrax}$: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAX