Thursday 24 October 2019, 14:00-17.00
Machine learning represents a radical new approach to many computational tasks which can be a valuable alternative to traditional physics-based simulation or multi-parameter data analysis. Additionally machine learning is being used to assist researchers by helping to direct and accelerate the exploration of parameter spaces, or even to accelerate directly computation itself.
This workshop is for researchers who would like to learn more about applying machine learning to their own field. We will start with speakers talking generally across multiple physical disciplines, before breaking into streams exploring materials research (including soft matter), plasmas and particle accelerators. Invited speakers include representatives from NVIDIA, the Alan Turing Institute and STFC, as well several university groups and the winner of the Computational Physics Thesis prize, Aldo Glielmo who used interpretable machine learning algorithms for two specific problems in condensed matter physics.
A multidisciplinary one-day workshop, the day is co-hosted by the Computational Physics group in collaboration with the Polymer Physics group, Particle Accelerators and Beams group and Plasma Physics group.
Richard Graham, University of Nottingham, UK
Alan Lowe, University College London, UK
Richard Clayton, University of Sheffield, UK
IOP Computational Physics Group
Computational physics may be broadly defined as 'the science of using computers to assist in the solution of physical problems, and to further physics research'.
Computers now play a role in almost every branch of physics and the following list provides some examples of areas that lie within the scope of computational physics:
IOP Particle Accelerators & Beams
The Particle Accelerators and Beams Group aim to promote the professional standing of workers in the field of particle accelerators, through exposure, events, outreach and increased academic profile.
The Group’s interest areas include, but are not restricted to, the following topics:
And many more.
11 October 2019