2026-06-06 –, Grand Hall 2
In science and engineering, we are frequently challenged by the inability to manipulate environmental variables—a key component of the scientific method. For example, we cannot simply stop a hurricane in its tracks or change the temperature of the Sun. Instead, we heavily rely on "Forward Models"—numerical simulations that predict data from physical parameters. However, these models are often massively computationally expensive.
Emulators (or surrogate models) present a solution. Whether solving a single time-sensitive equation or searching a high-dimensional inference space, emulators can accelerate simulation results by orders of magnitude. In this talk, we show how these machine-learning tools are revolutionising research across STEM disciplines, from inferring input parameters to developing digital twins and augmenting foundational models.
This talk aims to show how we can accelerate the solving of complex, imperfect, high-dimensional physical models using machine learning. We will be discussing:
- The motivation for accelerating models (3 mins)
- An introduction to emulation (5 mins)
- The most common emulation architectures (4 mins)
- Effective sampling and parameter selection for training data (4 mins)
- Model dimensionality reduction techniques and optimisation (4 mins)
- Emulator uncertainty quantification and inference techniques (4 mins)
- Designing an emulator workflow (2 mins)
- Data augmentation for existing datasets (5 mins)
- Use case examples (9 mins)
Attendees will gain a deep understanding of how to architect surrogate models and the libraries typically used to create them, enabling users to "fast-forward" their own computationally intensive numerical models.
Prerequisites: Basic familiarity with Python and regression concepts. No physics background required.
Austen is a computational astrophysicist specialising in scientific machine learning. Currently a postgraduate researcher at the University of Southampton, he will soon be joining the University of Cambridge as an Exoplanetary Data Science Research Associate. His work primarily focuses on accelerating complex physics simulations using "fast-forward" emulator techniques, which he has applied across diverse domains ranging from fusion-energy plasma control at the UK Atomic Energy Authority to extreme weather forecasting at IBM Research.