[nestorD]

TLDR: Using neural network to model physical systems as black boxes and then, later, using symbolic regression (genetic algorithm to find a formula that fits a function) on the model to make it explainable and improve its generalization capacities.

The system managed to reinvent Newton's second law and find a formula to predict the density of dark matter.

(note that symbolic regression is often said to improve explainability but that, left unchecked, it tends to produce huge unwieldy formulas full of magical constants)