Jeremy Yates and Frederik De Ceuster


The feedback loop between simulations and observations is the driving force behind almost all discoveries in astronomy. However, as technological innovations allow us to create ever more complex simulations and make ever more detailed observations, it becomes increasingly difficult to combine the two: since we cannot do controlled experiments, we need to simulate whatever we can observe. This requires efficient simulation pipelines, including (general-relativistic-)(magneto-)hydrodynamics, particle physics, chemistry, and radiation transport. In this talk, we explore the challenges associated with these modelling efforts and discuss how adopting data-driven surrogate modelling and proper control over model uncertainties, promises to unlock a gold mine of future discoveries. For instance, the application to stellar wind simulations can teach us about the origin of chemistry in our Universe and the building blocks for life, while supernova simulations can reveal exotic states of matter and elucidate the formation black holes.

Our speakers

Frederik De Ceuster obtained his PhD in Computational Astrophysics from University College London in 2022, on the development of novel algorithms for 3D radiation transport. He is currently a Postdoctoral Research Fellow of the Research Foundation Flanders (FWO), working at the Institute of Astronomy and the Leuven Gravitational Wave Centre (KU Leuven, Belgium), where he develops simulations of dying stars, and searches for new ways to confront these complex simulations with the current and next generations of ever richer astronomical observations.

Jeremy Yates is currently Professor of Data Science and Digital Research Infrastructure at the Computer Science Department at UCL. He is involved in astronomical interferometry, spectral line radiative transfer, the simulation of stellar winds and chemistry, the analysis and modelling of NHS health data, designing and provisioning national supercomputing systems and services such as STFC DiRAC (, and the pre-exascale UKRI DAWN AI for Research Resource (AIRR) system based at Cambridge (