Many domains in science use computer simulations to study an observed phenomenon. Consider for example physics models of particle movements, models of electrical activity in the brain, or modelling the spread of a disease. As these simulations become more and more complex, it becomes increasingly difficult to fit them to data, i.e. to find parameters such that the simulation output reproduces experimentally observed data. We develop methods that efficiently solve this problem by using neural networks that perform Bayesian inference. Read the article in the ML for Science Blog.