JaxDF

Built directly on JAX, it enables models to be pure functions compatible with JIT compilation and automatic differentiation, as demonstrated in the example where gradients are computed using JAX's AD.

Supports arbitrary discretizations like Fourier spectral methods, allowing researchers to tailor numerical models to specific needs, as highlighted in the overview and example.

Aimed at aiding in constructing numerical models for physical systems, with a focus on easy experimentation and integration into machine learning pipelines, per the philosophy section.

Models are pure functions, aligning with JAX's philosophy for efficient parallelization and compilation, as noted in the key features.