I have been working on a tool that aids in circuit tuning: model circuit equations as python functions, the solution space is discrete component values, auto solve for a target spec, build the circuits, record measurements, fit error, repeat until the experiment matches predictions. It adjusts nearly every parameter between tests and converges surprisingly fast. (25% to 2% error in 3 tests for an active band pass filter)

The MVP was hand coded, leaned heavily on sympy, linear fits, and worked for simple circuits. The current PoC only falls back to sympy to invert equations, switches to GPR when convergence stalls, and use a robust differential evolution from scipy for combinatorial search. The MVP works, but now I have a mountain of slop to cleanup and some statistics homework to understand the limitations of these algorithms. It’s nice to validate ideas so quickly though.