The goal of this project is to develop novel splitting methods for stochastic differential equations (SDE), tailored for use in a simulation-based inference framework to approximate posterior distributions. We additionally employ an invariant neural network, previously developed for Markov processes, to learn low-dimensional representations of SDE solutions. The neural network is incrementally retrained by exploiting an multi-round sampler, which provides new training data at each round