You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations. Typically, a model of the physical process in the form of differential equations is available but leads to intractable inference over its parameters. While the forward propagation of parameters through the model simulates the evolution of the system, the inverse problem of finding the parameters given the sequence of states is not unique. In this work, we propose a generalisation of the Bayesian optimisation framework to approximate inference. The resulting method learns approximations to the posterior distribution by applying Stein variational gradient descent on top of estimates from a Gaussian process model. Preliminary results demonstrate the method's performance on likelihood-free inference for reinforcement learning environments.
Output:
An approach to combine variational inference and Bayesian optimisation to solve complicated inverse problems