Generative Diffusion Models for Lattice Field Theories
Supervisor: Professor Biagio Lucini
Project description:
Lattice field theory provides a first-principles framework for studying strongly interacting systems, such as Quantum Chromodynamics (QCD, the theory of the strong interactions), via numerical simulations. The standard approach relies on Markov Chain Monte Carlo (MCMC) methods to compute physical observables. However, these applications to QCD are computationally very expensive, and affected by technical limitation such as long autocorrelation times and the freezing of topological sectors, which severely limit efficiency.
Inspired by non-equilibrium physics, generative diffusion models present an appealing alternative. These powerful machine learning tools can learn the underlying probability distribution of field configurations and generate new, statistically independent samples directly. This approach has the potential to drastically reduce computational costs and overcome the fundamental sampling issues afflicting traditional MCMC methods.
This project will formulate generative diffusion models in an unsupervised setting (crucial for the provision of a first-principle approach) for lattice field theories. The student will develop the methodology for their application to lattice gauge theories, aiming to create a robust framework for efficient ensemble generation in physically relevant theories such as QCD, to enable high-precision calculations in regions of parameter space currently inaccessible to standard methods.
Funding Notes:
This project is open to candidates applying for CSC/EPSRC/Underrepresented Studentships and self-funded candidates.
Further information:
How to apply
Entry requirements
Fees and funding
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