Author: Bart Vorselaars

Organised by the IOP Computational Physics Group and the IOP Higher Education Group 

Date of event 14 April 2021. Location online.

The impact of COVID-19 on the higher education sector was immediately disruptive. However, university instructors of computational physics met these unexpected challenges head on, delivering complex content to students in innovative ways. Now, we have the opportunity to reflect on what has worked and what has not in order to strengthen our community’s ability to deliver world-class education to the next generation of computational physicists. Lessons Learned in Lockdown will bring together lecturers, instructors and higher education professionals who have been involved in teaching numerical or computational physics in the face of COVID-19 disruptions. Jointly hosted by the IOP Higher Education and Computational Physics groups, the event will serve as a forum to exchange experiences and discuss lessons learned, as well as identify opportunities to advance the ways we teach numerical techniques to physics students. This online event is scheduled for 14 April 2021. 

Call for abstracts:

Abstracts are requested for short talks, interactive workshops and posters presenting student work e.g. project work. Abstracts of a maximum of 250 words should be submitted online by 1 March 2021. 

Key Dates:

Abstract submission deadline:   1 March 2021
Registration deadline:                13 April 2021

For more information on the conference, please visit https://www.iopconferences.org/iop/1643/home 

The IoP had an inaugural Physics in the Spotlight event from 21st-25th October 2019, celebrating the move to their new head quarters in King’s Cross with events organised by many groups together. On the 24th of October the Computational Physics group (CPG) hosted a one-day meeting on machine learning applications in physics. This was in collaboration with the Particle Accelerator and Beam group, the Plasma Physics group and Polymer Physics group.

The plenary sessions were a sell-out, hitting the 140 capacity of the lecture theatre in the new venue, showing the appetite and interest in the field. With keynote speakers from the Alan Turing Institute, STFC’s Scientific Machine Learning group, and an ex-accelerator scientist turned FinTech Machine Learning consultant who refreshed us on the journey machine learning had taken since his work with it on ion beam spectroscopy a decade ago, there was a strong and varied programme on forefront techniques. After a short break this was followed up by talks from Jacqui Cole (Cambridge) and Aldo Glielmo, the winner of the CPG thesis prize.

The afternoon was split into two parallel sessions. This allowed us to explore developments more specific to each group interests. The CPG organized one with the plasma physics group and accelerator physics group getting together to understand commonalities in large facility design and data exploration. Presentations by Matt King and Hannah Kockelbergh and a panel discussion featuring Stephen Dann set the scene for a discussion session, which overall highlighted the need for cross community training to help those looking to exploit ML and data-centric methods for physics.

The second parallel session was organized with the Polymer Physics group and focused on machine including deep learning in soft and biological matter. Presentations related to Gaussian processes were given by Richard Graham (Nottingham) and Richard Clayton (Sheffield). Applications to 2D or 3D image data were by Alan Lowe (UCL), Rollo Moore and Ladislav Urban (NIHR); Dimitris Pinotsis (City/MIT) presentation was on network architectures.

The day also featured the launch of the IoP Journal ‘Machine Learning: Science and Technology’. Alongside the introduction to the scope of the journal, there was a cake cutting at the end of the day. Discussions continued over refreshments into the evening.

The organizing committee consisted of Jonathan Smith (CPG/PABG), Gavin Tabor (CPG), Bart Vorselaars (CPG), as well as Joao Cabral and Nigel Clarke (both Polymer PG) and Greg Daly (Plasma PG). Furthermore, David Dunning (PABG) was also helpful in finding some of the speakers.

Machine Learning Day – Part of Physics in the Spotlight
Institute of Physics, London
Thursday 24 October 2019, 9.30-17.00

Organised by the IOP Computational Physics Group, IOP Polymer Physics Group, IOP Particle Accelerators and Beams Group and IOP Plasma Physics Group

We are pleased to announce a one-day event on Machine Learning in Physics. The meeting will take place on Thursday 24 October at the IOP, near King’s Cross in London, and is part of the Physics in the Spotlight week.

A general session is planned for the morning with two parallel focus sessions running in the afternoon: Machine Learning in soft and biological matter, and Machine learning in plasma and accelerator physics.

There are still slots available for contributed talks. Please email j.cabral@imperial.ac.uk for contributions related to Machine learning in soft matter, and Jonathan.smith@stfc.ac.uk for any other contributions. Deadline for contributions is the 3 October 2019.

Registration for the event is free. Please visit http://spotlight2019.iopconfs.org more information and to book your place.

Preliminary program of confirmed invited speakers:

Introduction to machine learning in physics with applications (morning session)
Armando Vieira – Hazy
Keith Butler – STFC
Jacqui Cole – University of Cambridge/Rutherford
Aldo Glielmo – KCL
TBC – Alan Turing Institute

Machine learning in soft & biological matter (afternoon session I)
Richard Graham – University of Nottingham
Alan Lowe – UCL
Richard Clayton – University of Sheffield

Machine learning in accelerator physics and plasma physics (afternoon session II)
Stephen Dann – Lancaster University/STFC
Tom Holmes – University of Sheffield