Turn SymPy expressions into trainable JAX expressions. The output will be an Equinox module with all SymPy floats (integers, rationals, ...) as leaves. SymPy symbols will be inputs.

Optimise your symbolic expressions via gradient descent!

pip install sympy2jax

Requires:
Python 3.7+
JAX 0.3.4+
Equinox 0.5.3+
SymPy 1.7.1+.

import jax
import sympy
import sympy2jax

x_sym = sympy.symbols("x_sym")
cosx = 1.0 * sympy.cos(x_sym)
sinx = 2.0 * sympy.sin(x_sym)
mod = sympy2jax.SymbolicModule([cosx, sinx])  # PyTree of input expressions

x = jax.numpy.zeros(3)
out = mod(x_sym=x)  # PyTree of results.
params = jax.tree_leaves(mod)  # 1.0 and 2.0 are parameters.
                               # (Which may be trained in the usual way for Equinox.)

sympytorch.SymbolicModule(expressions, extra_funcs=None)

Where:

• expressions is a PyTree of SymPy expressions.
• extra_funcs is an optional dictionary from SymPy functions to JAX operations, to extend the built-in translation rules.

Instances can be called with key-value pairs of symbol-value, as in the above example.

Instances have a .sympy() method that translates the module back into a PyTree of SymPy expressions.

(That's literally the entire documentation, it's super easy.)

Neural networks: Equinox.

Numerical differential equation solvers: Diffrax.

Type annotations and runtime checking for PyTrees and shape/dtype of JAX arrays: jaxtyping.

This is not an official Google product.