machine-learning

Active Building Centre

The Active Building Centre Research Programme is researching and developing innovative tools and technologies that will ensure buildings of all scales contribute to a reduction in carbon emissions and a more sustainable built environment.

Physically Constrained Machine Learning for Non-linear Spatio-temporal Partial Differential Equations

This project was an EPSRC feasibility study, looking at applying probabilistic machine learning approaches to non-linear partial differential equations, particularly the Cahn-Hilliard model for polymer demixing.

Deep Probabilistic Machine Learning

In this project, we developed scalable approaches for applying Gaussian process regression to multi-output signals with non-linear dependencies. This included using physics-based techniques, including series representation and ordinary differential equation solvers, combined with machine learning techniques such as GPs and autoregressive flows, to infer latent variables and forces.

Development and application of machine learning techniques for characterisation and quantification of change in time-lapse resistivity monitoring

This project was part of a collaborative effort and joint supervision with British Geological Survey addressing the need for automatic feature detection and interpretation 3D time-lapse images of subsurface geology obtained through electrical resistivity tomography.