Beyond backpropagation: JAX's symbolic power unlocks new frontiers in scientific computing- Google Developers Blog

This post details how researchers are leveraging JAX's composable transformations and Taylor mode automatic differentiation (AD) to solve high-order Partial Differential Equations (PDEs) efficiently. It highlights the limitations of standard deep learning frameworks (which rely on repeated backpropagation) for computing high-order derivatives and contrasts them with JAX's Taylor mode AD, which computes them in a single forward pass. The post introduces the Stochastic Taylor Derivative Estimator (STDE) method, which achieved over 1000x speed-up and 30x memory reduction for solving a 1-million-dimensional PDE. It also mentions the generalization of JAX to handle infinite-dimensional vectors (functions in Hilbert space) for calculating variational derivatives, a capability not found in other frameworks. The post emphasizes JAX's role as a foundational library for differentiable programming in scientific discovery.