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

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This post details how researchers are using JAX's Taylor mode automatic differentiation to solve high-order Partial Differential Equations (PDEs), achieving over 1000x speed-up and 30x memory reduction compared to baseline methods. It highlights the development of the Stochastic Taylor Derivative Estimator (STDE) method, which leverages JAX's ability to efficiently compute arbitrary differential operators in a single forward pass. The post also mentions the generalization of JAX to handle infinite-dimensional vectors (functions in Hilbert space) by describing them as a custom array and registering them with JAX, enabling the calculation of variational derivatives for functionals and operators.