First prize for this year’s 2022 IoP CPG Thesis Prize has been awarded to Zafiirah Hosenie, University of Manchester. Zafiirah’s thesis, titled Feature Detection and Classification in streaming and non-streaming astronomical datasets, applied machine learning techniques to the challenges that arise from the large, streaming, data volumes that are prevalent in modern Astronomy.
When classifying astronomical source types to their observed variations in brightness, there exists an imbalance: There are many class types that are rare but potentially quite interesting. Zafiirah enacted a rigorous statistical analysis of the features used to identify these systems, and developed novel machine learning approaches to deal with the class imbalance. Additionally, she worked with the real-time transient pipeline of the MeerLICHT telescope to resolve the problem of distinguishing between real transients and ‘bogus’ ones. Time-domain astrophysics, studying transient and variable stars, also allows astronomers to explore the Universe from a new perspective, and the algorithms Zafiirah developed have been successfully deployed at the MeerKAT radio telescope array and the MeerLICHT optical telescope, both in South Africa.
We look forward to reading more about Zafiirah’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Zafiirah’s thesis is available online.
Second prize for this year’s 2022 IoP CPG Thesis Prize has been awarded to Mary Coe, University of Bristol. Mary’s thesis, titled Hydrophobicity Across Length Scales: The Role of Surface Criticality, employed Monte Carlo simulations and density function theory to elucidating the behaviour of water near a hydrophobic solid surface. Despite its ubiquity in everyday life and in many scientific disciplines, the underlying physical mechanism relating hydrophobicity on the microscopic scale to hydrophobicity on macroscopic length scales has remained a difficult problem. Mary studied density depletion and enhanced fluctuations in the vicinity of the drying critical point for several fluid-fluid and fluid-solid interactions near curved surfaces, and so extended her work beyond hydrophobicity to consider more generally solvophobicity. Mary’s results provide strong numerical evidence that the mechanism underlying both hydrophobicity and solvophobicity across microscopic and macroscopic length scales is a drying surface critical point.
We look forward to reading more about Mary’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Mary’s thesis is available online.
Date of event 29 September 2022. Location UCL, London/Online.
The Fortran SG AGM will be on the 29th September, followed by an afternoon of presentations held jointly with the IOP Computational Physics Group. While both are hybrid events, you are still required to book even if not physically attending as the Zoom link will be distributed by the booking mechanism.
We are always seeking new and enthusiastic members for the committee and if this interests you please let the group secretary know on or before the 22nd September. The secretary is Sam Ellis <firstname.lastname@example.org>.
There are three talks in the afternoon, each allocated 50min with gaps for refreshments/breaks. Lunch will not be provided but we hope to have soft drinks & nibbles for the afternoon talks.
Schedule for the day (Times are London local, BST)
* AGM (10:30 – 12:00) * Lunch break / Networking (12:00 – 14:00) – NB Lunch will *not* be provided but there are many restaurants/pubs in the vicinity * Afternoon Talks (14:00 – 17:00) – Jointly held with the IoP Computational Physics group.
Talks running order (titles may be refined nearer the date):
* The new features of Fortran 2023 – John Reid (RAL) * Nvidia Fortran Compiler – Getting Fortran onto GPUs – Jeff Larkin & Jeff Hammond (Nvidia) * Fortran Lang Projects – FPM & others – Laurence Kedward (Bristol University)
Join us for the Machine Learning for Healthcare hybrid meeting by the Institute of Physics Medical Physics and Computational Physics Groups at the IOP Headquarters in London, King’s Cross, on Monday 21st of November 2022.
Machine learning is a game changing technology with the potential to revolutionise a wide range of healthcare applications. Early diagnosis and treatment are vital in improving overall patient outcomes. By automating and expediting imaging and treatment planning, machine learning in healthcare is a promising solution that can assist in meeting the demands of today’s growing workload. Developing, validating, and integrating new machine learning tools within the healthcare current infrastructure is not without challenges however. Storage, sharing, and privacy of patient health information are just some of the key issues.
Researchers from both academia and industry and healthcare practitioners are invited to participate in this one-day meeting. With an exciting line-up of invited speakers, the aim is to review the latest developments, benefits, and challenges of integrating machine learning into healthcare practice.
Quantum in the Summer is a free, week-long summer school for sixth form students introducing them to important concepts in quantum physics, as a taster for what it’s like to study physics at university. It includes theory talks, hands on experiments, lab tours, and evening social activities. This year it will run from 1-5 August. You can find more information on the website: http://www.bristol.ac.uk/qet-labs/outreach/quantum-summer/, or by email at email@example.com.
Living systems are continually in active motion. From global scale migration down to enzymatic conformational transitions and kinetic action, living systems self-organize by moving. Moreover, motility as a response to stimuli is a key strategy by which living organisms capitalize on opportunities and combat threats. Motion is then a characteristic hallmark of biological complexity; however, it is also fundamentally physical. This has made studying motility one of the most fruitful points of collaboration between biologists and physicists, and remains an exciting frontier for both groups.This workshop aims to stimulate new collaborative partnerships between experimental biologists and computational physicists. The programme is organized jointly by the IOP Biological and Computational Physics Groups and seeks to address: Biological questions that have yet to receive sufficient attention from computational modellers; Emerging numerical approaches with potential for simulating biological motions.
This year’s 2021 IoP CPG Thesis Prize has been awarded to Sarah Jenkins, University of York. Sarah’s thesis, titled Spin Dynamics Simulations of Iridium Manganese Alloys, develops an atomistic model of IrMn. This poorly understood material is antiferromagnetic and has been used in hard disk drives for some time; however, its physics at the atomic scale has not previously been well understood due to the complexity of the material’s structure. Sarah implemented a multiscale micromagnetic model within the open-source VAMPIRE simulation package. Sarah’s thesis presents her findings on IrMn alloys in three parts: (i) its ground state magnetic structure and thermal stability, (ii) its magnitude and magnetic anisotropy (iii) the interaction (exchange bias) at the interface with a ferromagnetic layer. Her results resolve the microscopic origins of exchange bias with potential impacts in future data storage, neuromorphic computing and antiferromagnetic spintronics.
We look forward to hearing about Sarah’s work in the CPG Talks Series and reading more about Sarah’s project in the next IoP Computational Physics Group Newsletter. In the meantime, Sarah’s thesis is available online.
The conference is the XXXII in a series of meetings of scientists working in the domain of Computational Physics. The registration is now open and we welcome researchers from all areas of Computational Physics to join us in this online event. More details can be found at the conference webpage below:
The spring 2021 edition of the newsletter is now available to read, with contributions from the 2020 IoP CPG PhD thesis prize winner Dr Javier Diaz, as well as conference and workshop reports and future events.
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.
Abstract submission deadline: 1 March 2021 Registration deadline: 13 April 2021