They model a system which they know to be constrained by a closed-form equation called the Hamiltonian. They (cleverly, IMO) force the network’s predictions to be constrained by the Hamiltonian, by choosing the right output and loss function.
I don’t see a way to generalize this to the procedural rule-based systems you describe, unless they too are governed by a fairly simple continuous function Like the Hamiltonian.
I don’t know if it was “dramatic”, but it made me really happy.
I don’t see a way to generalize this to the procedural rule-based systems you describe, unless they too are governed by a fairly simple continuous function Like the Hamiltonian.
I don’t know if it was “dramatic”, but it made me really happy.