Simulation intelligence (SI) subsumes an emerging generation of scientific methods which utilize digital simulations for emulating and understanding complex real-world systems and phenomena. Recently, neural networks and deep learning have demonstrated a great potential for accelerating and scaling up SI to previously intractable problems and data sets. However, the availability of user-friendly software is still limited, which hampers the widespread and flexible use of modern SI methods.

In this project, we focus on software for amortized Bayesian inference, which is an essential part of SI. The hallmark feature of amortized Bayesian inference is an upfront training phase (e.g., of a neural network), which is then amortized by a nearly instant fully Bayesian inference for an arbitrary number of data sets during test time. Concretely, we aim to advance the BayesFlow research software library into becoming the long-term, gold-standard software for amortized Bayesian inference.

Project Members: Lars Kühmichel

Funders: German Research Foundation (DFG)