Enhancing Synthetic Models with Artificial Intelligence
An online course on the principles of Simulation-Based Inference (SBI) will be organized by the Jülich Supercomputing Center on 7-8 September 2026.
This tutorial introduces SBI, a framework combining Bayesian modeling, AI techniques, and high-performance computing to address key challenges, such as performing reliable inference with limited data by using AI-based approximate Bayesian computation.
Moreover, it tackles the problem of intractable likelihood functions, thereby allowing the use of Bayesian inference for biological systems with multiple sources of stochasticity. The tutorial also demonstrates how to leverage HPC environments to drastically reduce inference runtimes, making it highly relevant for large-scale biological problems. This tutorial bridges theoretical foundations with hands-on applications realized via Jupyter notebooks.
This two-half-day course aims at scientists who are willing to speed up their Bayesian inference methods using AI-based tools and simulations, and to take their Bayesian inference to the next level by handling intractable likelihoods. The course is also intended for scientists who are willing to enhance their simulations with AI-based inference methods for uncertainty quantification.