We used physical symbolic regression to learn a universal crack tip detection algorithm. For this, we performed finite element simulations under several different load conditions. Then, we trained a deep symbolic regression model to learn a physically admissible formula for the crack tip correction vector given the calculated Williams coefficients as input. Finally, we successfully applied the discovered algorithm on experimental data obtained by digital image correlation.