State of the Art JAX

JAX, a Python library for high-performance numerical computation, is rapidly becoming a cornerstone for accelerating scientific computing across diverse fields. Current research focuses on leveraging JAX's automatic differentiation and just-in-time compilation capabilities to build efficient and scalable implementations of various models, including spiking neural networks, cellular automata, agent-based models, and reinforcement learning environments. This allows researchers to tackle previously intractable problems, such as large-scale simulations and high-dimensional Bayesian inference, leading to faster experimentation and more robust results in areas ranging from neuroscience and materials science to cosmology and finance.