$\texttt{immrax}$: A Parallelizable and Differentiable Toolbox for
  Interval Analysis and Mixed Monotone Reachability in JAX
Akash Harapanahalli Saber Jafarpour Samuel Coogan
We present an implementation of interval analysis and mixed monotone interval
reachability analysis as function transforms in Python, fully composable with
the computational framework JAX. The resulting toolbox inherits several key
features from JAX, including computational efficiency through Just-In-Time
Compilation, GPU acceleration for quick parallelized computations, and
Automatic Differentiability. We demonstrate the toolbox's performance on
several case studies, including a reachability problem on a vehicle model
controlled by a neural network, and a robust closed-loop optimal control
problem for a swinging pendulum.