If I understand them correctly, they're saying to use standard, optimization methods after writing a fitness or evaluation function to score your possible solutions. Which is a normal, non-SAT way of doing optimization.

So, you could use it for any application you saw benefit from genetic algorithms, simulated annealing, or tabu search. You can even use those to optimize neural networks without backpropagation and with fewer, local optima. Many papers on this but it's computationally heavier.