Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

 

Past events in this series


Thu, 21 Mar 2024

16:00 - 17:00
Virtual

Data-driven surrogate modelling for astrophysical simulations: from stellar winds to supernovae

Jeremy Yates and Frederik De Ceuster
(University College London)

The join button will be published 30 minutes before the seminar starts (login required).

Further Information
Abstract

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.

Thu, 28 Mar 2024

16:00 - 17:00
Virtual

Differential Equation-inspired Deep Learning for Node Classification and Spatiotemporal Forecasting

Noseong Park

The join button will be published 30 minutes before the seminar starts (login required).

Further Information
Abstract

Scientific knowledge, written in the form of differential equations, plays a vital role in various deep learning fields. In this talk, I will present a graph neural network (GNN) design based on reaction-diffusion equations, which addresses the notorious oversmoothing problem of GNNs. Since the self-attention of Transformers can also be viewed as a special case of graph processing, I will present how we can enhance Transformers in a similar way. I will also introduce a spatiotemporal forecasting model based on neural controlled differential equations (NCDEs). NCDEs were designed to process irregular time series in a continuous manner and for spatiotemporal processing, it needs to be combined with a spatial processing module, i.e., GNN. I will show how this can be done.