Austen Wallis
Austen Wallis is a post-graduate researcher at the University of Southampton, specialising in scientific machine learning. His work focuses on developing emulator frameworks that accelerate complex physical simulations by orders of magnitude, utilising high-performance computing and modern AI. While his primary research focuses are in astrophysics, Austen has successfully applied his "fast-forward" techniques across diverse fields, including fusion-energy plasma control at the UK Atomic Energy Authority to extreme weather forecasting at IBM Research. He is a strong advocate for open science and building interactive, reproducible tools using the PyData stack.
Session
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.