Beyond backpropagation: JAX’s symbolic power unlocks new frontiers in scientific computing

This content originally appeared on Google Developers Blog and was authored by Google Developers Blog

JAX, a framework known for large-scale AI model development, is proving to be a powerful tool in scientific computing, particularly for solving complex Partial Differential Equations (PDEs), now being leveraged by researchers to achieve significant speed-ups and memory reductions in solving high-order PDEs and demonstrating its potential to unlock new frontiers in scientific discovery.

This content originally appeared on Google Developers Blog and was authored by Google Developers Blog