Neural networks often have trigonometric functions internally, so it would be massively more computation than necessary. If you have a few spare CPU cycles, a

Neural networks often have trigonometric functions internally, so it would be massively more computation than necessary. If you have a few spare CPU cycles, a hybrid approximation could start with a sparse lookup table of values as the initial guess for a few rounds of a numerical approximation technique. Or you just store the first few coefficients of a polynomial approximation (as in the OP's work).