Deep neural networks don't help with problems where there is no solution; neural networks approximate functions where the explicit form of the function is unknown. For example nobody knows how to implement isCat(animal) without using machine learning, but we have enough data on cats to approximate said function without much trouble.
SAT solvers work out if it is possible to satisfy a logical model (hence the name).
In theory a neural network could approximate a function that solves SAT. In practice it is a much better idea to use a SAT solver. Neural networks aren't that powerful at solving logic puzzles.
SAT solvers work out if it is possible to satisfy a logical model (hence the name).
In theory a neural network could approximate a function that solves SAT. In practice it is a much better idea to use a SAT solver. Neural networks aren't that powerful at solving logic puzzles.