Why do you need a neural network when you have the Hamiltonian mechanics of the system modeled? I've always understood Langrangian/Hamiltonian mechanics to be methods of modeling the behavior of a system through the decomposition of the external constraints and forces acting on a body. In other words you can understand a complex model by doing some calculus on the less complex constituents of the model.
I'm probably misunderstanding what the accomplished, but it sounds like they've increased the accuracy of a neural network model of a system, notably for edge cases, by training it on complete a complete model of said system.
I'm probably misunderstanding what the accomplished, but it sounds like they've increased the accuracy of a neural network model of a system, notably for edge cases, by training it on complete a complete model of said system.