Symbolic regression has the same failure mode; the reasons why the model failed can be explained in a more digestible way, but the actual truth of what happened is fundamentally similar -- some coefficient was off by some amount and/or some monomial beat out another in some optimization process.
At least with symbolic regression you can treat the model as an analyzable entity from first principles theories. But that's not really particularly relevant to most failure modes in practice, which usually boil down to either missing some qualitative change such as a bifurcation or else just parameters being off by a bit. Or a little bit of A and a little bit of B.
At least with symbolic regression you can treat the model as an analyzable entity from first principles theories. But that's not really particularly relevant to most failure modes in practice, which usually boil down to either missing some qualitative change such as a bifurcation or else just parameters being off by a bit. Or a little bit of A and a little bit of B.