  Deep neural network (DNN) architectures are constructed that are the exact
equivalent of explicit Runge-Kutta schemes for numerical time integration. The
network weights and biases are given, i.e., no training is needed. In this way,
the only task left for physics-based integrators is the DNN approximation of
the right-hand side. This allows to clearly delineate the approximation
estimates for right-hand side errors and time integration errors. The
architecture required for the integration of a simple mass-damper-stiffness
case is included as an example.
