PhD Studentship: Fast Simulation Algorithms for Correlated Dynamics in Condensed Matter and Data Science: From Superconducting Films to Epidemic Modelling - #48860

University of Warwick

Date: 1 week ago
City: Coventry
Contract type: Full time



UK Students, EU Students, International Students

Fully funded

Full Time

7th February 2024

29th February 2024



Supervisors: Dr. Michael Faulkner (Warwick Centre of Predictive Modelling (WCPM), Engineering), Prof. Gareth Roberts (Statistics)

Summary: Recent experiments on superconducting films revealed strongly correlated dynamics at the superconducting transition, with evidence for the phenomenon also found in simulations of magnetic films and epidemic models. Modelling these effects is important for high-precision engineering of superconducting and magnetic films – as well as for predicting early-warning signs for epidemics – but this remains unresolved due to the challenges they pose to simulation. Through collaboration with a key Warwick data scientist, this project will develop state-of-the-art simulation algorithms to characterise correlated dynamics in all three systems – which we will corroborate in experiment with collaborators in Soochow (China), Warwick and Oxford.

Many of the most challenging problems in predictive modelling involve strongly correlated system dynamics. These effects freeze systems near random states for disproportionately long times, so that experimental observations disagree with prediction. One cause of correlated dynamics is critical slowing down (CSD). This occurs at continuous phase transitions across statistical science, where it is characterised by some autocorrelation time diverging with system size. For example, recent experiments on superconducting films revealed strong autocorrelations at the superconducting transition, and evidence for CSD has also been found in simulations of magnetic films (also implying its existence in colloidal films) and epidemic models.

This creates significant challenges for simulation because algorithms are often based on diffusive physical dynamics, so that CSD also freezes simulations at continuous phase transitions. This destroys their predictive power, but recent advances in computational physics and data science have led to new algorithms based on ballistic-style ‘superdiffusive’ dynamics, accelerating simulations of both superconducting/magnetic/colloidal films and epidemic models.

This project will exploit these algorithms to circumvent CSD and characterise its effects on superconducting, magnetic and colloidal films, working closely with experimental partners in Soochow, Warwick and Oxford to corroborate our results with superconducting, magnetic and colloidal-film experiments. We will work in parallel with our key data-science partner at Warwick to adapt his algorithms for epidemic modelling– allowing us to characterise CSD at the epidemic transition, to ultimately predict early-warning signs for epidemics.

This project brings together expertise from the Warwick Centre for Predictive Modelling, Warwick Statistics and Warwick Physics, along with various external partners who will provide input on both the advanced simulation algorithms and feedback from experiment.

Large-scale simulations will be performed on Warwick’s high-performance-computing infrastructure, and the applicant will develop a high level of research software engineering skills over the course of the project. There is also the potential for travel to visit external project partners.

Informal enquiries to *************** are welcome.

Additional Funding Information

Awards for both UK residents and international applicants pay a stipend to cover maintenance as well as paying the university fees and a research training support. The stipend is at the standard UKRI rate.

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