Computer simulations provide high-fidelity models and allow to generate insights without the need of running real experiments. They are especially useful in areas where real experiments would be too costly or even impossible. However, those simulations are not suited for doing inference on their parameters and pose challenging inverse problems. To overcome this constraint and allow the usage of high-fidelity simulators for statistical inference, the field of simulation-based inference explores methods to circumvent intractable and inaccessible likelihood terms and enables the combination of simulated data and observations obtained in a lab or elsewhere. The primary goal is to obtain a good approximation to the posterior distribution of parameters defining the data generating process.