We will identify stochastic differential equations (SDEs) for observables of fine-grained particle simulations in order to coarse-grain their dynamics. Simulations data will be obtained from Vadere for at least two simulations. We will approximate the drift and diffusivity functions in the SDE using a neural network. Our approach works on scattered snapshot data and is designed to handle different time steps per snapshot.