I don't know; I use isympy (interactive sympy, uses IPython if installed) as a local Wolfram Alpha for all my math problems, and it's really good at that.
It’s called Mathematica, son.
sympy.physics.vector (https://docs.sympy.org/dev/modules/physics/vector/index.html
and
sympy.physics.mechanics (https://docs.sympy.org/dev/modules/physics/mechanics/index.h...)
You can ultimately simulate and visualize dynamical systems:
https://pydy.readthedocs.io/en/latest/examples/multidof-holo...
Eventual goal was trying to implement something like XGBoost, but by applying successive regression equations to the residuals instead of successive decision trees. E.g. figure out that a sin wave best fits the initial data, and then a linear best explains the remaining difference.
Worked pretty well on toy data sets, but was much too slow to scale to anything real. Still think the idea has promise for producing human interpretable ML models.
These symbolic tools can save ones skin on a bad day.
With SymPy however (and some optional Newtonian physics package), one can simply define their free-bodies and coordinate systems, and SymPy will spit out simplified, compiled tensors of whatever expressions they want! It was truly magical at the time.