44 citations · 143 across the 17 of their papers we have counts for
19 papers
Cross-Validation Based Adaptive Sampling for Multi-Level Gaussian Process Models
Louise Kimpton, James Salter, Tim Dodwell +2
Complex computer codes or models can often be run in a hierarchy of different levels of complexity ranging from the very basic to the sophisticated. The top levels in this hierarch…
Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling
Joel Ho, Nick Pepper, Tim Dodwell
A probabilistic machine learning model is introduced to augment the turbulence model in order to improve the modelling of separated flows and the generalisability of lear…
A probabilistic peridynamic framework with an application to the study of the statistical size effect
Mark Hobbs, Hussein Rappel, Tim Dodwell
Mathematical models are essential for understanding and making predictions about systems arising in nature and engineering. Yet, mathematical models are a simplification of true ph…
Scalable multiscale-spectral GFEM with an application to composite aero-structures
Jean Bénézech, Linus Seelinger, Peter Bastian +4
In this paper, the first large-scale application of multiscale-spectral generalized finite element methods (MS-GFEM) to composite aero-structures is presented. The crucial novelty…
A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators
Nick Pepper, Marc Thomas, George De Ath +4
Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant…
Multilevel Delayed Acceptance MCMC
Mikkel B. Lykkegaard, Tim J. Dodwell, Colin Fox +2
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target d…