[modeless]

TL;DR: JAX (the GPU-accelerated Python ML framework)

Pretty cool to see applications of all this good stuff developed for ML in other areas! I guess a side effect is that the calculations should now be differentiable and you could optimize various parameters using gradient descent if you wanted. You could even use it as a component of a neural net and backpropagate gradients through it.

Also it's not clear if they used GPUs but they probably could for an even better speedup in a scenario with large batches of calculations.