Abstract: 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.

Abstract: A method for providing a biological logic gate comprising the following steps: subjecting a bistable autoregulatory gene network (GRN) to a noisy background; identifying adjustable parameters of the GRN; using logical stochastic resonance to determine values of the GRN parameters which result in the GRN performing different logic gate functions; and setting the parameter values of the GRN such that the GRN performs a first logic gate function.