Methods, systems, and computer readable media for utilizing an augmented neural network are disclosed. In one embodiment, the method includes utilizing a neural network (NN) pre-processor to convert generic coordinates associated with a dynamical system to canonical coordinates, concatenating a Hamiltonian neural network (HNN) to the NN pre-processor to create a generalized HNN, and training the generalized HNN to learn nonlinear dynamics present in the dynamical system from generic training data. The method also includes utilizing the trained generalized HNN to forecast the nonlinear dynamics, and quantifying chaotic behavior from the forecasted nonlinear dynamics to discover and map one or more transitions between orderly states and chaotic states exhibited by the dynamical system.
PHYSICS AUGMENTED NEURAL NETWORKS CONFIGURED FOR OPERATING IN ENVIRONMENTS THAT MIX ORDER AND CHAOS