21 citations · 47 across the 14 of their papers we have counts for
16 papers
Online Gate-Driven Flow Control in Resin Transfer Moulding Using a Neural-Network Surrogate
Nicholas Wright, Oliver Maclaren, Piaras Kelly +2
In resin transfer moulding, complete saturation of the fibre preform is necessary before the resin front reaches the outlet vent(s), to prevent dry-spot formation. In practice, the…
Multi-type Sensor Placement for PDE-based Bayesian Inverse Problems
Steven Maio, Alen Alexanderian, Karina Koval +1
We address optimal placement of multi-type sensors for Bayesian inverse problems governed by partial differential equations (PDEs). The proposed framework allows for sensors with d…
Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
Ruanui Nicholson, Matti Niskanen, Oliver J. Maclaren +1
We consider joint inversion for two or more unknown parameters from observational data in the Bayesian framework. Standard approaches often either treat the parameters as independe…
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry +2
Shape optimization under uncertainty (OUU) is computationally intensive for classical PDE-based methods due to the high cost of repeated sampling-based risk evaluation across many…
Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
Ruanui Nicholson, Radoslav Vuchkov, Umberto Villa +1
In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial dif…
Invariant Image Reparameterisation: Bridging Symbolic and Numerical Methods for Identifiability Analysis, Model Reduction, and Prediction
Oliver J. Maclaren, Ruanui Nicholson, Joel A. Trent +2
Structural and practical parameter non-identifiability issues are common when mathematical models are used to interpret data. Such issues motivate model reparameterisation and redu…
Ensemble Kalman Inversion for Geothermal Reservoir Modelling
Alex de Beer, Elvar K Bjarkason, Michael Gravatt +4
Numerical models of geothermal reservoirs typically depend on hundreds or thousands of unknown parameters, which must be estimated using sparse, noisy data. However, these models c…
Data Space Inversion for Efficient Predictions and Uncertainty Quantification for Geothermal Models
Alex de Beer, Andrew Power, Daniel Wong +7
The ability to make accurate predictions with quantified uncertainty provides a crucial foundation for the successful management of a geothermal reservoir. Conventional approaches…
Non-intrusive optimal experimental design for large-scale nonlinear Bayesian inverse problems using a Bayesian approximation error approach
Karina Koval, Ruanui Nicholson
We consider optimal experimental design (OED) for nonlinear inverse problems within the Bayesian framework. Optimizing the data acquisition process for large-scale nonlinear Bayesi…
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty
Alen Alexanderian, Ruanui Nicholson, Noemi Petra
We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs) under model uncertainty. Specifically, we co…
Joint Estimation of Robin Coefficient and Domain Boundary for the Poisson Problem
Ruanui Nicholson, Matti Niskanen
We consider the problem of simultaneously inferring the heterogeneous coefficient field for a Robin boundary condition on an inaccessible part of the boundary along with the shape…
Coupled Elastic-Acoustic Modelling for Quantitative Photoacoustic Tomography
Hwan Goh, Timo Lahivaara, Tanja Tarvainen +4
Quantitative photoacoustic tomography (qPAT) is an imaging technique aimed at estimating chromophore concentrations inside tissues from photoacoustic images, which are formed by co…
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Oliver J. Maclaren, Ruanui Nicholson
We consider basic conceptual questions concerning the relationship between statistical estimation and causal inference. Firstly, we show how to translate causal inference problems…
Incorporating Posterior-Informed Approximation Errors into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification
Oliver J. Maclaren, Ruanui Nicholson, Elvar K. Bjarkason +2
We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, `out-of-the-box' Markov chain Monte Carlo (MC…
An Additive Approximation to Multiplicative Noise
Ruanui Nicholson, Jari P. Kaipio
Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with…
Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field
Ruanui Nicholson, Noemi Petra, Jari Kaipio
We consider the reconstruction of a heterogeneous coefficient field in a Robin boundary condition on an inaccessible part of the boundary in a Poisson problem with an uncertain (or…