Publications (12)
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 lea…
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…
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…
An adapted deflated conjugate gradient solver for robust extended/generalised finite element solutions of large scale, 3D crack propagation problems
Konstantinos Agathos, Tim Dodwell, Eleni Chatzi +1
An adapted deflation preconditioner is employed to accelerate the solution of linear systems resulting from the discretization of fracture mechanics problems with well-conditioned…
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
Gianluca Detommaso, Tim Dodwell, Rob Scheichl
In this paper, we present a generalisation of the Multilevel Monte Carlo (MLMC) method to a setting where the level parameter is a continuous variable. This Continuous Level Monte…
Novel design and analysis of generalized FE methods based on locally optimal spectral approximations
Chupeng Ma, Robert Scheichl, Tim Dodwell
In this paper, the generalized finite element method (GFEM) for solving second order elliptic equations with rough coefficients is studied. New optimal local approximation spaces f…
Context-Aware Generative Models for Prediction of Aircraft Ground Tracks
Nick Pepper, George De Ath, Marc Thomas +2
Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based,…
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…
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1
Learning representations that capture both intrinsic data geometry and target-relevant structure remains a fundamental challenge, particularly in settings where data reduction must…
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
Andrew Corbett, Archit Sood, Anna Tzatzopoulou +2
Recent work on recursive architectures has shown that tiny neural networks can be surprisingly powerful on structured reasoning tasks. The trick is to model reasoning trajectories…
dune-composites -- A New Framework for High-Performance Finite Element Modelling of Laminates
Anne Reinarz, Tim Dodwell, Tim Fletcher +3
Finite element (FE) analysis has the potential to offset much of the expensive experimental testing currently required to certify aerospace laminates. However, large numbers of deg…
High-performance dune modules for solving large-scale, strongly anisotropic elliptic problems with applications to aerospace composites
Richard Butler, Tim Dodwell, Anne Reinarz +3
The key innovation in this paper is an open-source, high-performance iterative solver for high contrast, strongly anisotropic elliptic partial differential equations implemented wi…