2026-08-13 –, Room 2
We present a Bayesian calibration framework built on Turing.jl for statistically rigorous data assimilation. To scale inference with Gaussian Processes, we employ a Bayesian Committee Machine approach, and exploit parallelism across both CPU and GPU backends.
We demonstrate the framework on both analytical and real-world data from accelerator physics, highlighting speedups.
The result is a practical, flexible toolkit designed for rapid model updating and calibration.
Data assimilation is an essential step in bridging the gap between simulation and experiment, playing a central role in iterative experimental design. Bayesian Calibration provides a statistically rigorous approach to infer simulation parameters. In experimental settings, i.e. accelerator physics, practitioners need inference frameworks that are not only adaptable but also fast enough to inform the next round of measurements.
We present a Bayesian calibration framework built on Turing.jl, leveraging its composable and easily modifiable model specification to accommodate a wide range of experimental configurations and problem settings. The framework includes hierarchical modelling to capture the full parameter posterior over multiple experiments. To scale inference with Gaussian Processes, we employ a Bayesian Committee Machine approach, and we exploit parallelism across both CPU and GPU backends. Sampling is performed using NUTS and Metropolis-Hastings.
We demonstrate the framework in both analytic test cases and real-world data from accelerator physics, highlighting achievable speedups, and discussing current limitations and roadblocks for GPU-accelerated sampling in Turing.jl. The result is a practical, flexible toolkit designed to sit inside the experimental iteration loop enabling rapid model updating and calibration.
I'm a PhD student in the Accelerator Modelling Group at the Paul Scherrer Institute. My work focuses on Bayesian statistics and data assimilation, leveraging Julia and the Turing.jl, in the context of particle accelerator experiments.