5 citations · 5 across the 3 of their papers we have counts for
7 papers
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…
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…
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…
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…