140 citations · 1.9k across the 86 of their papers we have counts for
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Bayesian Assessments of Aeroengine Performance with Transfer Learning
Pranay Seshadri, Andrew Duncan, George Thorne +3
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements both along and between ax…
Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches
Rebecca Ward, Ruchi Choudhary, Alastair Gregory +2
Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin is a true representation…
Integration in reproducing kernel Hilbert spaces of Gaussian kernels
Toni Karvonen, Chris J. Oates, Mark Girolami
The Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from a numer…
Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests
Ashley Scillitoe, Pranay Seshadri, Mark Girolami
Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community. However, the introduction of a machine learning (ML) model introduces a new sour…
Semi-Exact Control Functionals From Sard's Method
Leah F. South, Toni Karvonen, Chris Nemeth +2
The numerical approximation of posterior expected quantities of interest is considered. A novel control variate technique is proposed for post-processing of Markov chain Monte Carl…
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne, François-Xavier Briol, Mark Girolami
Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultan…